Geometrically guided exemplar

Geometrically guided exemplar
c 2011 Society for Industrial and Applied Mathematics
Vol. 4, No. 4, pp. 1143–1179
Geometrically Guided Exemplar-Based Inpainting∗
Frédéric Cao†, Yann Gousseau‡, Simon Masnou§, and Patrick Pérez¶
Abstract. Exemplar-based methods have proven their efficiency for the reconstruction of missing parts in a
digital image. Texture as well as local geometry are often very well restored by such methods. Some
applications, however, require the ability to reconstruct nonlocal geometric features, e.g., long edges.
In order to do so, we propose to first compute a geometric sketch, which is then interpolated and used
as a guide for the global reconstruction. In comparison with other related approaches, the originality
of our work relies on the following points: (1) The geometric sketch computation is parameter-free
and based on level lines, which provides a complete, reliable, and stable representation of the image.
(2) The completion of the geometric sketch is fully automatic. It is done using a new—and interesting
on its own—geometric inpainting approach that interpolates level lines with Euler spirals. Euler
spirals are natural curves for shape completion and have been used already for edge completion and
inpainting. It is the first time, however, that these curves are used for completing the whole level
lines structure. (3) The general reconstruction is performed using a guided version of a classical
exemplar-based method. However, we do not constrain the exemplar-based reconstruction to strictly
follow the geometric guide. We actually use a new metric between blocks that consists of the sum
of the classical L2 metric between any two blocks of the general image plus an L2 metric between
the corresponding blocks in the completed geometric image. This is equivalent to a Lagrangian
relaxation of a strictly guided reconstruction. We discuss in the paper the details of the method and
some related mathematical issues, and we illustrate its efficiency on several examples.
Key words. inpainting, texture synthesis, exemplar-based inpainting, level lines, image geometry, Euler spirals
AMS subject classifications. 68U10, 94A08, 65D05, 65D17, 65D18, 65K10
DOI. 10.1137/110823572
1. Introduction and motivations. The last 12 years have witnessed many contributions
to the inpainting problem in digital images, i.e., the problem of recovering entire regions
where the information either has been lost or is partially occluded by undesired objects. The
two main applications of inpainting methods are image restoration (e.g., the suppression of
scratches and blotches in old pictures and movies) and image manipulation (e.g., the removal
of objects in photo editing or movie postproduction), and some inpainting strategies have also
been used for digital zooming, color demosaicing, video deinterlacing, superresolution, and
There are basically three categories of methods in the literature. The variational or PDE∗
Received by the editors February 7, 2011; accepted for publication (in revised form) August 5, 2011; published
electronically December 1, 2011. This work was supported by ANR Project FREEDOM.
DxO Labs, 92100 Boulogne-Billancourt, France ([email protected]).
Telecom ParisTech, LTCI CNRS, 75013 Paris, France ([email protected]).
Université de Lyon, CNRS, Université Lyon 1, Institut Camille Jordan UMR 5208, 69622 Villeurbanne Cedex,
France ([email protected]).
Technicolor Corporate Research, Rennes Laboratory, F-35576 Cesson-Sévigné Cedex, France ([email protected]
based approaches enable in favorable cases a good reconstruction of the global geometry of
an image but fail at correctly synthesizing the texture parts. In contrast, the methods of
the second category were initially designed for texture synthesis. Some of these that we will
describe later, called exemplar-based, can produce visually striking results but often do not
permit the interpolation of nonrepetitive global image structures such as long edges. Finally,
methods in the third category do not involve any explicit interpolation in the image domain
but rather in one or several transform spaces, relying, e.g., on wavelets or Fourier transform.
They usually perform well for sparse missing data or thin domains.
Recently, there have been several attempts to combine variational/PDE methods with
exemplar-based algorithms in order to deal with those situations where the latter alone fail at
recovering the geometry. This is the spirit of the method presented here that first completes
the geometry for a simplified version of the image and then uses it as a weakly constraining
guide for a complete reconstruction. More precisely, a sketch of the image is first obtained
by selecting relevant level lines. Then, the missing part of the sketch is interpolated by
prolongating interrupted level lines in a variational way. Eventually, the interpolated sketch
is used as a guide in an exemplar-based inpainting method. A first advantage of the proposed
methodology is that each one of these three steps relies on very few parameters. Second, it is to
our knowledge the only approach permitting in favorable cases the simultaneous reconstruction
of both the geometry and the texture over very large missing regions. This will be illustrated
in the experimental section through challenging experiments.
2. State of the art.
2.1. Geometric methods. The different approaches to geometrical inpainting are all
based on the prior that geometry can be recovered from the close neighborhood of the region
to be filled (referred to as the inpainting domain hereafter). They divide into two categories:
1. the variational methods that involve a criterion of regularity of the reconstruction;
2. the pure PDE methods that try to diffuse and/or advect progressively the information
from the boundary.
The first variational approach to geometrical inpainting was proposed in 1998 in [65] (see
also [64]), following an inspiring work by Nitzberg, Mumford, and Shiota [69] on amodal
completion and depth computing based on Kanizsa’s vision theory [56]. The basic idea of [65]
is that the geometry can be reconstructed in the inpainting domain by simply interpolating—
using short and not too oscillating curves—all level lines that touch the domain boundary.
This happens to be equivalent to minimizing a functional involving the curvature to a power
p. Further details will be given in section 4. A globally minimizing scheme is proposed
in [65] for the case p = 1, while the local minimization in the case p > 1 is addressed in [21]
using a fourth-order equation. An interesting relaxed formulation of a slightly different model
is proposed in [6] that yields a numerically more convenient second-order Euler–Lagrange
A totally different approach has been proposed by Bertalmio et al. in [9], where the term
inpainting has been proposed for the first time in this context. The idea is to mimic the way
professionals do inpainting for real painting restoration. It consists in progressively advecting
the valid information from the boundary of the inpainting domain inwards using a third-order
advection-type equation. This equation transports the image values along continuations of
edges, an additional anisotropic diffusion being used to avoid shocks. An interesting interpretation of the model is given in [14], and the connections with the classical Navier–Stokes
equation of fluid dynamics are shown in [8].
Using a more global approach, Chan and Shen propose in [22] a denoising/interpolation
model based on the joint minimization of a quadratic fidelity term and a total variation criterion. This latter actually makes sense only for thin inpainting domains because its minimizers
are images having the shortest level lines. A variant of the associated Euler–Lagrange equation
is studied in [23].
Elder and Goldberg propose in [37] an approach to image editing based on the manipulation of the edge map. A possible application is the removal of objects in some simple
situations: the corresponding edges are first removed from the edge map, the remaining edges
are completed if necessary with, possibly, the help of the user, and the final image is obtained
by a linear interpolation out of the edges.
Esedoglu and Shen propose in [39] interpolating in the inpainting domain using a piecewise
smooth function that minimizes the Mumford–Shah functional with an additional penalization, so that the discontinuity set has small Euler elastica energy (see below), i.e., is short and
not too curvy; see [38] for an interesting implementation of the model.
Grossauer and Scherzer study in [43] an inpainting model based on the Ginzburg–Landau
equation. In [84], Tschumperlé and Deriche propose an efficient second-order anisotropic
diffusion equation that preserves curvature and gives good results. The approach of Auroux
and Masmoudi in [5] uses the PDE techniques that have been developed for the inverse
conductivity problem in the context of crack detection. In [3, 76], a prior segmentation of the
edges outside the inpainting domain is performed. Then edges are interpolated—using splines
in [3], arc of circles in [76]—and the inpainting is completed using a smooth interpolation
between edges. The tricky part of such an approach is the careful choice of pairs of edges that
will be connected. Interestingly, the interpolation of large domains in [3] involves an additional
patch repetition procedure that turns the method into one of the very first geometry-guided
inpainting methods.
Finally, in an attempt to improve the fast but limited method proposed by Telea [81],
Bornemann and März describe in [14] a first-order equation that advects the image information
along the integral curves of a coherence vector field that extends inside the inpainting domain
the dominant directions of the outside gradient. They propose a very fast numerical scheme
based on fast marching. In terms of speed and quality of the results, it is one of the best PDE
approaches to the inpainting problem, together with [84].
There is, however, a common drawback to all PDE or variational methods that we have
presented so far, and that explains why they are used mainly for inpainting thin regions: they
are unable to properly restore textured regions, in contrast to the approaches that we will
now describe.
2.2. From texture synthesis to inpainting: Exemplar-based methods. For a long time,
texture synthesis has been formulated as a problem of estimating and then sampling a probability distribution [11, 29, 92]. This approach may provide good results but is usually computationally expensive, and, more importantly, the choice of a suitable model is a very difficult
task. De Bonet studies in [12] a multiresolution technique where interscale dependencies of
texture samples are constrained. Drawing their inspiration from psychophysics experiments,
Heeger and Bergen [51] propose synthesizing a texture by constraining the marginals of overcomplete wavelet decompositions. This work has been followed by others in which not only the
marginals but also more complicated statistics on wavelet decompositions are constrained; see
[79]. All these methods may provide good results but not for all situations: highly structured
patterns are often badly synthesized.
New models were proposed in the late 90’s that sample the values of a new texture directly
from a set of neighborhoods of a given sample image [33, 87]. Exploiting the locality and the
stationarity at a certain scale of the texture, they achieve in favorable cases a degree of realism
that was beyond the reach of previous approaches. The first celebrated algorithms in the class
of exemplar-based methods, due to Efros and Leung [33] and Wei and Levoy [87], both involve
the notion of patch, i.e., a square window of size r × r. The basic idea is that in order to
synthesize the value at a pixel whose neighborhood is partially known, one looks for similar
patches in a sample image (when performing texture synthesis) or in a valid part of the image
(when performing inpainting). The gray or color value at the pixel is simply chosen as the
value of the central pixel of the most resembling patch or sampled from a set of very similar
patches, the similarity between patches generally being measured with a weighted L2 metric.
Such approaches enable the reproduction of textures even when they are highly structured.
They are, however, prone to either garbage growing (the production of erratic structures) or
plain repetitions of the input sample. This core algorithm is proposed with different variants
in [33, 87] (see also [50, 13]), involving, for instance, a multiscale approach and a careful
synthesis order.
All these papers are essentially dedicated to texture synthesis, yet an example of inpainting is given in [33]. Previously, a parametric synthesis approach had been proposed in [54]
for illustrating an inpainting application. To the best of our knowledge, the first explicit and
systematic dedication to inpainting of a nonparametric synthesis method can be found in [13].
Despite the great improvements with respect to the previous contributions to texture synthesis, a well-known problem of the exemplar-based methods that we have mentioned comes from
the synthesis of only one pixel at a time, which may sometimes result in “cycling” effects like
the constant propagation of an erroneous synthesis or the formation of much too repetitive patterns. Surprisingly, because it amounts to reducing the space of interpolation possibilities, better results can be obtained by synthesizing not only the central pixel but entire patches. The
use of such methods also considerably reduces the computational time. Many contributions
have been proposed in this direction with several variants [34, 47, 61, 28, 32, 72, 55, 90, 59, 2]:
• on the location of the patches to be synthesized (using a fixed grid or not, with either
no overlap, a smooth overlap, or a sharp overlap between adjacent patches),
• on the filling order (raster scan, concentric layers, incorporation of geometric or intensity constraints),
• on the searching domain (reinitialized at each iteration or constrained by the previously
chosen sample patch),
• on the similarity measure between patches and the way to find a minimizing candidate
(enumeration, belief propagation, etc.),
• on the artificial extension of the space of samples by introducing rotated and rescaled
versions of the existing patches.
Recently, the PatchMatch method [7] that involves random associations between patches
yielded spectacular improvements of the computational complexity. Let us finally mention
the recent contributions [40, 73, 57, 1], where the exemplar-based inpainting is formulated as
a variational problem with two unknowns, the image color and the similarity weights between
pairs of points, resulting in a more tractable numerical task.
State-of-the-art exemplar-based methods are able to produce, in a very reasonable time,
results that are often amazing. Most approaches are not only able to reproduce a texture but
are also very good at restoring the local geometric information whenever it can be obtained
elsewhere in the image. However, nonlocal geometric features cannot be well restored in
general, which was the main motivation for this paper.
2.3. Optimizing in a transform domain. There have been in recent years several contributions where the inpainting problem is formulated as an optimization task in a transform
domain, e.g., Fourier, wavelets, framelets, etc. It usually amounts to finding the reconstructed
image whose representation in the transform domain is optimal. For instance, a simultaneous geometry/texture inpainting is performed in [36] (see also [45, 46]) by optimizing the
decomposition in two different transform domains, one giving a sparse representation of the
geometry (the curvelet domain) and the other adapted to the texture (the DCT domain).
Other approaches in this category are [52, 17, 24]. All these methods are usually very efficient
when the unknown part of the image is a sparse set of pixels. In cases where the inpainting
domain is large, however, they are clearly outperformed by exemplar-based methods.
2.4. Combining texture synthesis and geometric reconstruction. Several papers on inpainting have been concerned with the combination of a PDE or variational model for the
reconstruction of the geometry together with an exemplar-based algorithm for the reconstruction of texture. In [10], the image is first decomposed into a geometric component and a
texture component using a technique described in [86]. The geometric component is inpainted
by the model of [9], the texture component is restored using the Efros–Leung algorithm [33],
and the final image is the sum of both reconstructions. An analogous approach is used in [74]
in the very specific context of detecting and removing text on an image. A limitation when
using such decompositions to perform the reconstruction is the nonindependence of the two
components. Independently inpainting these two components yields artifacts.
There is no explicit geometry/texture decomposition in [32] but rather a prior fast estimate of the colors of the hidden region by a multiscale filtering approach that guides the
exemplar-based inpainting. Let us mention that in a related direction, an early multiscale
inpainting method relying on the Laplacian pyramid was proposed in [70]. Using a guide for
the reconstruction is also the spirit of [31], except that the guide is obtained with the very
efficient PDE model of [84] (that we presented above) for geometrical inpainting. Analogously,
[75] proposes an algorithm for structure and texture restoration that incorporates a texture
synthesis algorithm to the geometric algorithm described in [76].
The exemplar-based approach of [28] has been an inspiration for our paper. It indeed
involves a local geometrical guiding through a priority term that forces the reconstruction
of strong edges first. In the current paper, we build instead a global geometrical guiding,
enabling the restoration of missing parts over large distances.
Similarly to the approach presented in this paper, several works propose performing in-
painting by relying on a segmentation of the image, either automatic or not, to infer the
missing geometry. The method presented in [55] consists of a prior edge segmentation in the
known part of the image followed by a completion of the edges in the inpainting domain. This
yields a partition of the domain that guides the final reconstruction done with a tensor voting
technique, yielding excellent results. In [80], the user manually draws the geometric structure
in the inpainting domain; then exemplar-based inpainting is performed, first along the edges
and then in the rest of the domain. Eventually, the method proposed in [25] also relies on
a prior computation of a sketch, and the sketch edges are interpolated using straight lines
or conic curves. This work is along the same lines as ours, but each part of the algorithm
is described in an imprecise way, precluding its practical use. For a different purpose, the
authors of [48] introduced a way to compute Marr’s primal sketch associated with an image
but also define a notion of sketchability and a way to resynthesize an image given a sketch
and a parsimonious modeling of texture.
Last, the method proposed in [16] uses a decomposition of image into a texture component
and a structure component [86], and combines the geometric model from [84] and the Efros–
Leung model [33] to inpaint, using a local isotropy criterion to decide whether a point should
be treated as texture or structure.
2.5. Contribution of this paper and reader’s guide. The method presented in this paper
is in the spirit of some approaches described above. Our motivation is to propose an algorithm
that exploits the performances of the best exemplar-based methods in addition to the ability
to recover long-range geometric structures, possibly more complex than straight lines. The
first step of our method is the creation of a simplified representation of the valid part of
the image, a sketch, that essentially contains the important geometric features (section 3).
This is done by a suitable selection of a few level lines wherever the image is significantly
contrasted. Following [30], a contrast is declared significant depending on a threshold that is
automatically, and parameter-freely, computed over the image. In contrast with the method
in [25], our sketch is easy and fast to compute, and it does not require any edge precompletion
since all curves in the image are level lines and are therefore closed curves.
In a second step (section 4), the sketch is reconstructed within the inpainting domain using
an inpainting method that is interesting on its own. It consists of the application to all level
lines of the same principle that guides the amodal completion process, a well-known ability
of our visual system. A similar strategy has been proposed in [65, 64] using straight lines (or
shortest polygonal curves in case of nonconvex inpainting domain) for completing the level
lines. The novelty here is the use of Euler spirals and the possibility of handling crossings. As
will be further explained in section 4, Euler spirals are natural curves for shape completion.
They have been used in [58] to design a simple inpainting model that consists of a prior
completion of broken edges followed by a smooth interpolation in between the reconstructed
edges. This method seems applicable only for images with few strong edges. In contrast, our
model performs the completion of all level lines and is therefore applicable, in theory, to any
image. Yet the completion principle that is the basis of our approach makes sense mostly for
the recovery of geometric features but is essentially not adapted for textures.
A key property of the new inpainting model of section 4.3 is the ability to handle sharp
discontinuities correctly, which is a very difficult task for any usual PDE approach. The
method in [65, 64] already had this ability, but the new method has the additional capacity
to handle curvy discontinuities and discontinuity crossings.
We saw in the previous section that joint texture/geometry approaches are of two types:
the class of methods that independently process the geometry component and the texture
component, and the class of methods that use the prior reconstruction of the geometry as
a strict guide for the final reconstruction. We propose in section 6 a kind of Lagrangian
relaxation of the guiding, where the completed geometric image is used in an energy term
that is added to the classical patch metric of [72]; see section 5. We will provide the reader
with a few experiments showing that this Lagrangian guiding significantly improves the results
when some long-range, global geometry indeed has to be reconstructed, while it is more flexible
than other approaches where the guiding is strict.
We also address in this paper a theoretical question. As we previously mentioned, the
exemplar-based methods seem to have the ability to restore the geometry only locally. This
is, however, a heuristic statement that had not been theoretically justified so far. We give a
partial theoretical argument in that direction in the appendix, where we investigate the ability
to asymptotically recover a curve starting from patches made of straight lines.
Overall, the method presented in this paper allows, with very few parameters, the inpainting of a rich class of images containing texture and long-range geometry with possibly sharp,
curvy, and mutually crossing discontinuity lines. The experimental results that we obtain
confirm the pertinence of our model.
3. Computing a sketch of the image. By a sketch of a gray level image (the color case
will be addressed later on), we mean a piecewise constant approximation obtained with a
suitable segmentation procedure. The segmentation used in this paper is derived from the
topographic map of the image [19] and, more precisely, from the meaningful level lines defined
in [30] (other segmentation procedures could be used to compute a sketch). Recall that the
topographic map of a gray level image I is the collection of all its level lines that we define here
as the connected components of the boundaries of upper levels sets Xt (I) = {x : I(x) ≥ t}
(one could equivalently consider the lower level sets). Since
I(x) = sup{t : x ∈ Xt (I)},
the image can be completely reconstructed from its level lines. This complete representation
has several advantages. First, it is invariant with respect to local increasing contrast changes,
and, second, the topographic map can be associated with a nested tree structure that is very
useful not only for image segmentation [30] but also for denoising [67], shape extraction, and
comparison [63] or image compression [42].
A sketch of the initial gray level image is obtained by keeping only the most significant
level lines, i.e., those that are contrasted enough according to an a contrario criterion [30].
Roughly speaking, meaningful lines are lines having a contrast that is very unlikely to be
encountered in a white noise image. This amounts to keeping lines L for which
N.H(c(L))l(L) ≤ ,
where N is the total number of level lines in the image, H is the cumulative histogram of the
modulus of the gradient over the image, l(L) is the length of the line, c(L) is the minimum
Figure 1. A digital image (top), its meaningful level lines (bottom left), and the sketch reconstructed
only from these lines (bottom right). The texture has partly disappeared, but the geometric structures and the
dynamics remain.
contrast along the line, and is a positive number. In practice, we use a refinement of this
approach as presented in [18]. It consists in using a local estimation of the cumulated gradient
histogram H to assert the meaningfulness of a curve. We chose to use this refinement because
it yields less detection in textured area, a desirable property for the application considered
in this paper. We took = 1 for all the experiments to be displayed in this paper, drawing
on the robustness of a contrario approaches to the choice of this parameter. Eventually, we
obtain a piecewise constant image (a sketch) from the collection of selected lines as follows.
Relying on the nested structure of the topographic map, we simply define the value of the
sketch at a pixel x as the level associated with the shortest level line that encloses x.
Note that extracting the most significant level lines does not mean extracting the objects
in the image but constructing a piecewise constant image whose dynamics is very close to
the original. However, as observed in [63], boundaries of objects usually locally coincide with
pieces of level lines, so that the most salient objects will appear in the sketch representation.
The above procedure for computing a sketch is valid for gray level images only. It must
be emphasized, though, that most of the geometric information of real world color images
is carried by the luminance channel [20] (in classical chrominance/luminance representations,
such as Lab or YUV). For the exemplar-based method to be presented in section 5, a geometric
guide will be computed by interpolating a gray level sketch. This guide will be obtained from
the luminance channel of the original image to be inpainted (the YUV color model has been
used in all experiments presented below).
An example of a sketch is displayed in Figure 1. For the purpose of visualization, each
piecewise constant region of the sketch has been given the average color value computed over
the corresponding region in the original image. One may observe in this experiment that
microtextures and many small details do not appear in the sketch, while the geometry and
macrotextures remain, which is desirable from the perspective of the “geometric” inpainting
methods to be introduced in the next sections. Nevertheless, one cannot guarantee that
the sketch image will always be purely geometric: Figure 2 illustrates a situation where some
useless details still remain. In this very case, it does not alter the quality of the final inpainting
results that can be seen in Figure 18.
Figure 2. The sketch image (right) associated with this picture of a wood wall is not perfectly geometric. It
still contains a few useless details. Nevertheless, the final inpainting results are of good quality, as can be seen
in Figure 18, where cropped subimages are shown.
The situation in Figure 3 is more critical: branches and leaves form an in-between micro-/
macrostructure that is partially captured in the sketch image. The resulting image is too
complex to be properly inpainted by a geometric method, as shown in Figure 15.
At this point, it is worth noticing that any segmentation method enabling the creation of a
piecewise constant image could be used to produce the sketch, possibly yielding better results,
depending on the image. We chose the above method based on meaningful level lines for three
reasons: first, it gives fair results for the various experiments that we have done. Second, this
method relies on level lines, like both geometric inpainting methods to be introduced in the
following sections. Finally, the a contrario approach enables us to perform all experiments
using the same setting, namely = 1, for producing the sketch.
The next section is devoted to the description of two geometry-oriented inpainting methods
that will be used for the reconstruction of the sketch image. The first method relies on straight
lines (section 4.2), while the second one relies on Euler spirals (section 4.3). Both are based
on the interpolation of the broken level lines.
4. Level line completion, image inpainting, and sketch reconstruction. We describe in
this section two geometrical inpainting methods, i.e., two methods essentially devoted to the
reconstruction of geometry. The first one, introduced in [65], is based on the completion of
level lines by straight curves (or polygonal lines when the inpainting domain is not convex),
whereas the second approach is new and uses Euler spirals as completion curves. We will
see at the end of the section and further in the paper that both methods can be used for
the reconstruction of a sketch image aimed to be a geometric guide for the global inpainting
algorithm to be presented in section 6.
Figure 3. Branches and leaves form a micro-/macrostructure that is not properly captured in the sketch
image. This is a typical situation where a pure patch-based approach is better than a geometry-guided approach,
as illustrated in Figure 15.
Before proceeding, let us explain how the methods, initially designed for gray level images,
can be applied to color images. For these images, the notion of level line is not defined. Given
a method that interpolates the broken level lines of a scalar image, the reconstruction of a color
image will be performed by independently processing the luminance and both chrominance
channels in a suitable luminance/chrominance representation (YUV, Lab, etc.). Note that
using a luminance/chrominance representation instead of the usual RGB is crucial for limiting
the creation of false colors. Even though the methods will be applied on a gray level sketch in
the present paper, such an extension to color images may have an interest in other contexts.
We now show how an inpainting method can be derived from shape completion techniques.
4.1. From amodal completion to inpainting. Recall that, given a regular curve γ in the
plane parameterized by arc-length, the elastica functional1
|κγ (s)|2 ds,
where κγ denotes the curvature and L(γ) the length of the curve, measures the elastic energy
stored by the curve. It was first studied in 1744 by Euler in his work on the physics of thin rods.
Much later, it was proposed by Ullman [85] and Horn [53] in the context of shape completion.
Recall that our visual system has the ability to reconstruct missing edges when objects are
partially occluded; see the important contribution of Kanizsa [56] on the so-called amodal
completion. In terms of computer vision, the problem can be rephrased as follows: what is the
most “pleasing” curve that joins two points with prescribed tangent boundary conditions?
More general versions are
(α + β|κγ (s)|2 )ds.
The elasticae, i.e., the curves minimizing the elastica energy, are natural candidates since
they are the smoothest curves in regard to the accumulated elastic energy. In addition, as
shown by Ullman [85], they fulfill several reasonable axiomatic requirements (isotropy, locality,
smoothness, etc.)
Elasticae are, however, hard to compute, for they do not admit any analytical expression [68]. There have been many attempts to derive efficient approximation methods, either
L(γ) 2
|κ| ds, which is scale invariant;
for the elastica energy itself or for variants such as L(γ)−1 0
see [60, 69, 78, 82, 83, 91, 26, 15, 77, 35].
Although nice completions can be obtained with the elastica model, it has some drawbacks,
such as the computational issues we have mentioned but also the incapacity to recover angles
or circles or the nonexistence of a minimizer in some extreme boundary conditions [27, 62].
Other models have therefore been proposed, considering, however, that no model can always
provide the perfect solution, essentially because the problem is ill-posed: the question of
whether a curve is “pleasing” or not is somewhat subjective, and nothing totally reliable
can be said in some extreme situations. We mention, however, two other models that yield
interesting solutions in most common situations:
1. The minimal path/good continuation model of Fantoni and Gerbino computes an
intermediate solution between the straight line and the polygonal line obtained by
simply continuing the tangents from each extremity; see [41], where an exhaustive
survey of completion models is provided.
2. The Euler spiral—also called Cornu’s spiral or the clothoid loop—has been studied by
Euler in his work on freely coiled up elastic springs and accurately plotted by Cornu
in 1874. In the relaxed position of the spring—modeled as a curve γ—the scalar
curvature at each point satisfies κ(s) = C1 s + C2 , where C1 , C2 are constants and s is
the arc-length. Euler spirals have already been used in the context of shape completion
and for the inpainting of simple configurations; see, for instance, [58].
Splines are other somewhat natural candidates, and they have been used very frequently for
curve interpolation; see, for example, [3]. Out of the lack of rotational invariance, they are
actually less pleasing visually, in the context of curve completion, than Euler elasticae or
spirals; see, for instance, the interesting [49] on three-dimensional curve completion.
Let us now come to the issue of computational complexity. Completing one broken curve
using the Fantoni–Gerbino model or using Euler spirals is not difficult. A more difficult task is
to complete many curves because there are n! possible pairwise connections of 2n interrupted
lines. Is there a particular family of connections that makes sense in the context of image
inpainting? Can it be obtained without using an algorithm with exponential complexity? Let
us start with a model that has been initially proposed in [65, 64] and that involves straight
lines as very basic interpolating curves.
4.2. A simple inpainting algorithm using straight lines [65, 64]. The algorithm described in [65, 64] is shown in Table 1. It provides, for each channel in a chrominance/luminance
representation of a color image (e.g., YUV), a global discrete minimizer of an energy that penalizes the integrated absolute curvature, not the squared curvature, along interpolating level
lines. The algorithm involves the notion of a causal set of connections between compatible
T-junctions of a gray level image. We introduce both notions in the following definition. For
Table 1
Algorithm from [65, 64] for the geometrical inpainting of images using straight lines without crossings.
Inpainting with straight lines (without crossings) [65, 64]
Input: A color image I known outside an inpainting domain A.
Output: An inpainted image I.
For each channel in a luminance/chrominance representation of I:
1. extract all T-junctions on ∂A, i.e., termination points of level lines (see Definition 4.1);
2. compute the associated tangents, either by a simple averaging approach like in
[65, 64] or using a more robust method (see section 4.3 and [14]);
3. use dynamic programming to find a collection of shortest curves {γi }i∈{1,...,N}
that realize a causal set of pairwise connections between compatible T-junctions
γi1 , γi2 (see Definition 4.1) and minimize a linear combination of the curves’ total length and total curvature, including the angles at the extremities, i.e., the
N (α + β|κ|)ds + β(angles at γi1 and γi2 ) ;
4. find the piecewise constant function I˜ canonically associated with these interpolating level lines.
simplicity, we assume that the gray level image I, defined outside the inpainting domain A,
takes finitely many values, on a finite number of components, which is natural when dealing
with images. Note, however, that the definition could be extended, with additional technical
details, to the class of functions of bounded variation.
We will also assume that A is strictly contained in Ω. For the treatment of inpainting
regions that touch the image borders, a prior naive one-pixel extension of the image domain
can be performed that appears to be sufficient in practice: every valid pixel along the image
border is replicated towards the exterior. Every other pixel to be set (those that are connected
to a border pixel belonging to the inpainting region) is given the value of the closest valid
border pixel.
Definition 4.1 (T-junctions, compatibility, and causality). We call a T-junction any point x ∈
∂A on the inpainting domain’s boundary that is a termination point for the level line ∂{y ∈
A, I(y) ≥ I(x)}. This notion is the counterpart for the level line framework of the notion of
T-junction introduced by Kanizsa [56] to name the points where the edge of a partially occluded
object encounters the edge of an occluding object.
Being ∂A oriented counterclockwise, two T-junctions x, y ∈ ∂A are compatible if I(x) =
I(y) and (∇I(x)·T∂A (x))(∇I(y)·T∂A (y)) < 0, where T∂A (x), T∂A (y) denote the unit positively
oriented tangents to ∂A at x and y, respectively. The compatibility property ensures that a
curve joining x and y within A can be considered as the interpolation of a level line; see
Figure 4.
A system of pairwise associations between compatible T-junctions is said to be causal
whenever there exists a collection of curves realizing the connections without crossings (see
Figure 4. T-junctions are marked with black dots. Two T-junctions are compatible if any curve that connect
them within the inpainting domain can be considered as an interpolating level line; this is equivalent to assuming
that image values at the T-junctions are the same and that image gradients have compatible orientation.
Figure 5. Left: a causal system of pairwise connections between T-junctions: there exists a collection of
curves that realize the connections without crossings, for instance the collection of shortest paths as in the figure.
Middle: one can easily deduce a function from a causal system of connections between T-junctions and from a
collection of noncrossing curves realizing the connections. Right: for a noncausal system of connections, there
exists no system of curves that realize the connections without mutual crossings between some of them.
Figure 5).
Causality, minimality, and existence of crossings are different notions: it is easy to build
an example of a causal system of curves that realizes the connections without crossings but
is not optimal. One can also build an example of a noncausal system of associations between
compatible T-junctions and a related set of curves (necessarily with crossings) that minimizes
criterion (4.2).
However, it is easy to check that, given a causal set of associations between compatible
T-junctions, the shortest paths that realize the connections do not mutually cross. Moreover,
among all collections of curves realizing this same set of connections, the shortest paths have
minimal energy. This is obvious regarding the length term. As for the curvature term (i.e.,
the integrated absolute curvature plus angular defects), it follows from the fact that along any
curve joining two T-junctions and living in A, the angular total variation (which is exactly
the curvature term plus the angles at extremities) is necessarily not smaller than along the
shortest path. Note that this property is no more true if another power of curvature is used.
Eventually, given a causal system of pairwise associations between T-junctions and a
family of curves, not necessarily straight lines, that do not mutually cross and that realize
the connections, it is easy to deduce an interpolated function. Each curve can be seen as an
interpolating level line. For each value t ∈ {t1 , . . . , tM } taken by the function I on ∂A, one
defines At as the set enclosed by all the interpolating level lines at level t. By causality, the
sets At are nested; i.e., At ⊃ As if t < s. Indeed, if it were not the case, it would imply that
some interpolating curves have crossings, which contradicts the causality assumption. Then,
one defines the inpainting for each x ∈ A as
I(x) = t1 +
(ti+1 − ti )½Ati (x) .
In the next section, we will present a new method that yields an inpainting solution in the
case where crossings between interpolating curves are allowed, the set of connections being
causal or not.
In practice, how can one build a causal system of connections between compatible Tjunctions and how can one find a causal system of curves (actually of straight lines; see above)
that minimizes (4.2)? It suffices to observe that, once two compatible T-junctions have been
associated, they induce a partition of ∂A into two intervals. The only way to maintain the
causality is to impose that only T-junctions of the same component can be associated and to
iterate the construction. There is a direct link between the minimization under this causal
principle and dynamic programming: the size of a partition interval can be identified with
a dynamic variable, and the minimization within an interval of ∂A is independent of the
minimization within the complementary interval [65, 64]. Therefore, despite the limitations
of the model (optimal curves are straight lines if the inpainting domain is convex, polygonal
lines otherwise), it produces interesting results in a reasonable time. Instead of an exhaustive
search with exponential complexity, the global minimization of (4.2) can be performed by
dynamic programming in polynomial time. More precisely, the algorithm proposed in [65, 64]
runs in O(N 3 ) with respect to the number N of T-junctions, thus approximately in O(M 2 )
with respect to the number M of pixels in the inpainting domain.
In the next section, we describe a new geometrical inpainting approach using Euler spirals
for the completion of broken level lines. We already mentioned that Euler spirals have been
used in [58] for the design of a simple inpainting model. In contrast, our method can be used
in much more general situations and, because all partially occluded level lines are interpolated,
the resulting inpainted image is richer than what may be produced with [58]. In particular,
our approach can handle situations where the natural completion produces crossings between
4.3. A new inpainting method using Euler spirals. Let us first discuss the computation
of a Euler spiral joining two points with prescribed tangents at both ends. Without other
constraints, in particular if loops are allowed, there are infinitely many candidates. We shall
therefore select a spiral that minimizes the Euler elastica energy. Interestingly, there is actually
a close connection between elasticae and Euler spirals as explained in [27]. Parameterizing
a curve γ by arc-length and denoting as ψ the inclination at γ(s), i.e., the oriented angle
between the x-axis and the tangent γ (s), the elastica functional reads as
|ψ (s)|2 ds,
from which the following Euler–Lagrange equation can be derived, taking into account the
transversality condition due to the unknown length L(γ) [27]:
(ψ (s))2 = λ cos ψ(s) + μ sin ψ(s).
After linearization, one gets (ψ (s))2 = λ + μψ(s) whose solutions satisfy
ψ(s) =
(s − α)2 − ,
where α is a constant of integration. Since the scalar curvature coincides with ψ (s), we
deduce from the derivation of (4.5) that its solutions are Euler spirals, i.e., curves whose
curvature is an affine function of arc-length. Therefore, Euler spirals are the solutions of the
linearization of the Euler–Lagrange equation associated with the elastica energy. Euler spirals
have interesting properties: they include circles, they are rotationally invariant in contrast to
splines, and they can serve as C2 transitions between a segment and an arc of circle, which
explains why they are frequently used in very concrete applications, e.g., architecture, road
design, typography, and even for the design of roller coasters! Integrating (4.5), one obtains
that the curve can be parameterized as
sin(ψ(t))dt .
(x(s), y(s)) = (x0 , y0 ) +
This expression involves the Fresnel integrals
cos(t ) dt and S(x) =
C(x) =
sin(t2 ) dt
that can be easily approximated from their Taylor expansion. Different methods have been
proposed to find a numerical approximation of the Euler spiral joining two points with tangent
boundary conditions [66, 15]. Here we use the rather simple approximation algorithm proposed
in [27] that involves an iterated Newton scheme. Additional constraints are necessary to avoid
solutions with loops, but even in that case uniqueness is not always guaranteed, at least in
a few extreme situations: one can easily find two spirals with the same energy that join the
point (0, 0) to (1, 0) with initial direction (1, 0) and final direction (−1, 0). In this kind of
extreme situation, one may force a particular solution by imposing to the tangent along the
curve a suitable range of allowed directions.
Let us now describe the adaptation to Euler spirals of the algorithm presented in the
previous section. A first important point—for it influences the shape of the spirals a lot—is a
careful computation of the tangent at each T-junction of the “broken” level lines. We follow
the method due to Bornemann and März [14] after the work of Weickert [88, 89] on the robust
determination of coherence directions in an image. Denote as Ω the image domain, A the
inpainting domain, and I an image on Ω \ A. The coherent direction at x ∈ A is defined as the
normalized eigenvector associated with the minimal eigenvalue of the structure tensor [14]:
Kρ ∗ ½Ω\A ∇Iσ ⊗ ∇Iσ (x)
J(x) =
Kρ ∗ ½Ω\A (x)
where ½Ω\A is the characteristic function of Ω \ A, Kρ is a Gaussian smoothing kernel with
standard deviation ρ, and Iσ is defined by
Kσ ∗ ½Ω\A I
Iσ =
Kσ ∗ ½Ω\A
It can be experimentally observed that this computation yields a reliable estimation of the
level line directions at the T-junctions (a sensible choice for the convolution parameters is
σ = 1.5, ρ = 4).
The second important point is how one can handle several possibly crossing Euler spirals.
We mentioned in the previous section that such a problem does not occur with straight lines
if the set of connections is causal. If we now use Euler spirals, it is easily checked that even
the causality constraint no longer guarantees that any two curves will not mutually cross.
In addition, there are cases where noncausal connections, and therefore crossing curves, are
the right solution (see Figure 6). Since crossing situations can be somewhat puzzling, as in
Figure 7, the definition of an enclosed set from a collection of interpolating lines must be
done carefully. This is the purpose of the method presented in Table 2 and illustrated in
Figure 7. This method is the core of our new inpainting algorithm for the restoration of gray
level images based on Euler spirals that is described in Table 3. Again, the generalization to
color can be done by independently processing the three channels in a luminance/chrominance
color space.
Figure 6. A binary situation (left) where one can reasonably expect a completion with crossing curves
(right). The left figure shows how T-junctions are oriented in the algorithm of Table 2.
Figure 7. An illustration of how the algorithm of Table 2 works in a puzzling situation where some interpolating curves mutually cross (left figure) and make the reconstruction difficult. The algorithm of Table 2
provides a systematic way to decide whether a point of the inpainting domain should be colored in gray or in
white. The left figure shows several T-junctions connected by curves. In this situation, the algorithm of Table 2
yields three cycles, one of them being represented in the second figure, with the corresponding winding numbers.
The third figure shows the set of points with a positive winding number. Doing the same for both other cycles
and gluing the corresponding enclosed sets yields a reasonable result.
The reason why we use formula (4.12) comes from a classical result: any measurable
positive function I : Ω → R satisfies, at almost every point x ∈ Ω,
I(x) =
½{y: I(y)≥t} (x)dt.
Table 2
A method for constructing an enclosed set from a collection of oriented
curves joining paired compatible T-junctions, possibly with crossings
Inputs: A collection of T-junctions on ∂A, {j1 , j2 , . . . , j2N }, each of them endowed with
an orientation, 0 or 1, depending on the orientation of the gradient on the associated level line. We take as a convention that, being ∂A oriented counterclockwise,
the orientation at a junction ji is 1 whenever the gradient at j1 is oriented like
∂A, 0 otherwise (see Figure 6).
A collection of N curves, {γ1 , . . . , γN }, joining pairs of compatible T -junctions,
i.e., with opposite 0 − 1 orientations (see Figure 6).
Output: A set Aγ ⊂ A “canonically” enclosed by the curves and representing an interpolation in A of the outer level set associated with the level lines.
1. Step 1 : build a collection of closed curves (Ci ) linking T-junctions with alternating
labels 1, 0, 1, 0, etc., and obtained by gluing alternatively a curve γi and a piece
of ∂A. The building process is as follows. Pick a T-junction with label 1, call it
t1 , and let n = 1:
(a) Find the next T-junction on ∂A (walking counterclockwise), which necessarily has label 0; call it tn+1 ; n ← n + 1.
(b) Find the T-junction linked by a curve γi to tn ; call it tn+1 (it has label 1);
n ← n + 1.
(c) If tn = t1 , stop; else go to step (a).
One can check that such a process will necessarily build a maximal cycle. Call
it C1 , and remove all visited T-junctions from the list. Start from a remaining
T-junction with label 1 to build C2 , etc.
2. Step 2 : for each Ci define its canonical interior Ai as all points in A with a
positive winding number (index) with respect to Ci . There are many ways to
compute the winding number at a point with respect to a discrete oriented curve,
not necessarily simple. We use the simple oriented crossings-check procedure
of [44]a .
3. Step 3 : let Aγ = i Ai .
See also 0103/algorithm 0103.htm for a
Obviously, if u takes values in [m, M ], then
½{y: I(y)≥t} (x)dt,
I(x) = m +
and (4.12) follows. Note, however, that, contrary to what happens with the level sets {y :
I(y) ≥ t}t≥0 of the function I, our family of reconstructed sets At need not be nested;
therefore (4.12) is not the exact counterpart of (4.9). Yet our tests show that it is a reasonable
choice, much more sensible, for instance, than the formula I(x) = sup{t : x ∈ At }, which is
very sensitive to completion errors that might occur at the highest levels.
4.4. Results and application to sketch reconstruction. We present in Figures 8–10 three
examples of geometrical inpainting using either straight lines or Euler spirals. We illustrate
Table 3
Algorithm for the inpainting of gray level images with Euler spirals. The inpainting of color images can be
done by independently processing each channel in a luminance/chrominance representation, e.g., YUV.
Inpainting with Euler spirals (and possibly crossings)
Input: An image I : Ω \ A → R with A ⊂ Ω an (open) inpainting domain.
Output: An inpainted image I˜ : Ω → R.
1. Assume that I takes values {N1 , . . . , Nn+1 } on ∂A. Extract all T-junctions on
∂A, i.e., points of ∂{y ∈ ∂A, I(y) ≥ t} for t ∈ {N2 , . . . , Nn+1 }.
2. Compute the tangents of the associated level lines by the Bornemann–März–
Weickert method described above.
3. For each level t ∈ {N2 , . . . , Nn+1 } represented on ∂A:
(a) Extract the set of all T -junctions at level t, and let 2nt denote its (necessarily
even) cardinality.
(b) Find a set of Euler spirals (γjt )j∈{1,...,nt } that pairwise connect compatible
T-junctions with tangency constraints and minimize
nt (4.11)
(α + β|κ|2 )ds.
The minimization is performed over all configurations only if nt is low,
due to exponential complexity (in practice nt ≤ 6 is a reasonable choice).
Otherwise the minimization is performed over causal configurations only
(see section 4.2 for a brief account of how it can be done with dynamic
programming). In contrast with the algorithm of section 4.2, curves may
cross even for causal configurations since we are no longer limited to straight
(c) Use the algorithm in Table 2 to build a set At from the collection of interpolating curves.
4. Define the inpainted function I˜ as
if x ∈ A,
⎨ I(x)
(Ni − Ni−1 )½ANi (x) otherwise.
⎩ N1 +
in particular in Figure 10 how the technique naturally applies for reconstructing a geometric
sketch obtained with the algorithm described in section 3.
It now remains, in order to design a geometry-guided inpainting method, to combine the
sketch completion with an exemplar-based inpainting algorithm. We describe in the next
section the particular instance of exemplar-based method that we will use.
5. A closer look at exemplar-based inpainting. In this section we detail a robust and
efficient exemplar-based inpainting algorithm, the “patchwork” algorithm introduced in [72],
that we will use later on. The inpainting is performed by copying entire patches taken in
the valid part of the image, which allows a much better reconstruction of simple geometric
structures in comparison with the methods that involve only one pixel at a time. In the
appendix we provide a result that helps understanding, at least partially, how a generic patch-
Figure 8. Top: original color image with an occluded zone. The transparency of the occlusion is for
information only. Only the outer information is used for the reconstruction. Bottom: result after inpainting
with straight lines [65] of each channel in a YUV representation.
Figure 9. Left: Uranus in infrared light (courtesy STScl and NASA) with artificial occlusions. Right: after
reconstruction with Euler spirals of each channel in a YUV representation.
based inpainting method handles the geometric information.
We consider a color RGB image I = (I1 , I2 , I3 ) defined on a discrete domain Ω ⊂ Z2 , and
we denote as A ⊂ Ω the inpainting domain. The image norm at every pixel x is I(x) =
i=1 Ii (x). Given p ∈ N, the patch Γp (x) of a point x ∈ A is defined as the discrete
(2p + 1)2 -square neighborhood centered at x. With q > p ∈ N we associate the ring Bp,q (x) =
Γq (x) \ Γp (x). If G ⊂ Z2 , ∂G denotes its inner boundary, that is, the set of points that are in
G and have at least one neighbor in its complementary set Gc . We also consider the set of all
centers of neighborhoods that are included in Ac ,
Eq = {x ∈ Ω : Γq (x) ⊂ Ac }.
Eventually, we consider the set of patches intersecting A and lying on a regular grid with a
mesh of size (2p + 1)2 . That is, we define
Figure 10. A geometric sketch (see section 4.3) and its reconstruction using Euler spirals.
(5.2) Fq = {Γp (x), x = (k(2p + 1) + p, l(2p + 1) + p) , k, l ∈ Z, Γp (x) ∩ A = ∅}.
The basic idea of the algorithm is that the unknown parts of patches from Fq will be sequentially updated using a dictionary made up of patches centered at points of Eq .
Table 4
Exemplar-based inpainting algorithm [72] for a color RGB image (I1 , I2 , I3 ).
Exemplar-based inpainting algorithm [72]
1. Let Γp (x0 ) be a patch in Fq such that the intersection between Bp,q (x0 ) and Ac
has maximal area (a selection criterion such as the one proposed in [28] could
also be used).
2. Let y0 be defined as
y0 = argmin
I(yi ) − I(x0 − y + yi ) ;
yi ∈Bp,q (y)
yi −y+x0 ∈Ac
i.e., y0 is the center of the patch in Ac whose associated ring is the most similar
to the known part of the ring associated with the patch centered at x0 .
3. For each x ∈ Γp (x0 ) ∩ A, let I(x) = I(y0 + x − x0 ), and remove x from A.
4. Replace Fq with Fq \ Γp (x0 ), and go to 1 if Fp = ∅.
Given two integers p < q, the algorithm consists in replacing the unknown part of a patch
Γp (x) intersecting A with the values of the patch Γp (y), where y is such that the distance
between the rings Bp,q (x) and Bp,q (y) is minimal. In other words, the comparison and the copy
involve different pixels and the fact that Bp,q (x) encloses Γp (x) avoids blocky effects. Indeed,
the patches progressively inpainted by the algorithm do not overlap, but the associated rings
do overlap. This is an important issue, for blocky effects are a noticeable drawback of many
exemplar-based methods. The algorithm is summarized in Table 4.
This method gives impressive results on natural images, as may be seen in Figure 11. It
essentially depends on two parameters: the size (2p + 1)2 of the patches to be copied and the
size (2q + 1)2 of the neighborhood used to quantify the similarity of two patches. Usually,
small values of p yield good results for weakly structured texture images. The case p = 0
corresponds to the original algorithm of Efros and Leung [33]. Larger values of p give better
results in the presence of geometric structures. Indeed, large patches enable the propagation
of straight edges, whereas small values of p may lead to the creation of erratically oscillating
structures. However, even though edges are somehow preserved when using large patches,
two problems remain for achieving a satisfying reproduction of geometry. First, exemplarbased methods are local or semilocal, and therefore long edges crossing the missing part of
the image are often not satisfactorily restored (see the appendix for partial theoretical results
on the connections between patch-based inpainting and geometry reconstruction, and [4] for a
more detailed study from a related perspective). Second, curved edges are not well prolongated
if a suitable curvy piece of edge cannot be found elsewhere in the image. A solution would be
to enlarge the dictionary of patches by simply taking rotated versions. This sometimes yields
nice results, but, in general, erratic results often occur when the dictionary becomes too rich.
Figure 11. Top: original image with missing parts (holes) in white. Bottom: reconstructed image using
the exemplar-based method from [72] (with p = 4 and q = 6).
6. Using the reconstructed sketch to guide the exemplar-based algorithm: A Lagrangian formulation. Recall that the main motivation for this paper is to adapt an exemplarbased algorithm in order to improve the quality of the restored geometry, and in particular to
properly handle long-range geometric features, while maintaining a good ability to reproduce
the texture. We saw in section 3 how to compute a sketch of the image outside the inpainting
domain and in section 4 how to interpolate it, using either straight lines or Euler spirals. Let
us now examine how the reconstructed sketch can be used for guiding the exemplar-based
algorithm of the previous section. Again, we denote by A the inpainting domain and by
I = (I1 , I2 , I3 ) the RGB image known only on Ω \ A . We write ISg for the interpolated sketch,
a gray level image known on the whole image domain Ω. More precisely, ISg denotes the
interpolation of the gray level sketch obtained from the luminance channel in a YUV representation of the original image I. As already mentioned (see also [20]), the luminance channel
essentially contains all the geometric information of almost every real world image; therefore
using color sketches is unnecessary or even potentially harmful because of false colors.
Based on the interpolated sketch ISg , the core algorithm of our approach is given in Table 5.
Roughly speaking, given a point x on ∂A and using the notations of section 5, the algorithm
searches for y ∈ Ac such that, simultaneously, the following hold:
• the patches Γq (x) and Γq (y) in the luminance channel of the (previously reconstructed)
sketch are similar;
• the (known parts of the) rings Bp,q (x) and Bp,q (y) in the current image I = (I1 , I2 , I3 )
are similar.
Let us comment on the use of the term “Lagrangian”: we do not impose as a hard constraint
that two related points x0 and y0 must have the same neighborhood in the sketch image.
Instead, we rather use it as a soft constraint by introducing an additional term, of Lagrangian
type, that penalizes the discrepancy between the neighborhoods.
Table 5
The Lagrangian-type geometry/texture inpainting algorithm proposed in this paper. ISg denotes the sketch
that has been previously reconstructed on the whole image domain, and (I1 , I2 , I3 ) is the RGB image that is
progressively inpainted.
Geometrically guided exemplar-based inpainting algorithm
1. Let Γp (x0 ) be a patch in Fq such that the intersection between Bp,q (x0 ) and Ac
has maximal area (a selection criterion such as the one proposed in [28] could
also be used).
2. Let y0 be defined as
y0 = argmin λ
2 g
IS (yi ) − IS (x0 + yi − y)
yi ∈Γq (y)
+ (1 − λ)
2 .
I(yi ) − I(x0 + yi − y)
yi ∈Bp,q (y)
x0 +yi −y∈Ac
3. For each x ∈ Γp (x0 ) ∩ A, let I(x) = I(y0 + x − x0 ), and remove x from A.
4. Replace Fq with Fq \ Γp (x0 ), and go to 1 if A = ∅.
The parameter λ weights the respective contributions of the sketch and the original image.
Taking λ = 0.5 was a reasonable choice for all the experiments presented hereafter, which
explains why we will not discuss the correct tuning of this parameter. We nevertheless believe
that understanding correctly how λ must be chosen according to the very nature of the image
is an interesting challenge.
7. Experimental results.
Parameter setting. The first parameter involved in the complete inpainting procedure is
, driving the segmentation step. As is common for a contrario methods, it has been set to
= 1 for all experiments. As said before, we have no rigorous way to set the parameter λ,
balancing the geometry and texture contributions. The value λ = 0.5 appears reasonable and
has been kept for all experiments. As for the “patchwork” step, parameters have also been
fixed to p = 4 and q = 6, respectively.
Experiments. We display in Figures 12–21 several examples of image reconstruction where
we systematically compare the approach proposed in this paper with the original “patchwork”
algorithm from [72]. In a few experiments, we also compare with the methods proposed in [90]
and [57], as well as with the recent Photoshop’s “content aware fill” that is a nice extension
of [90] combined with [7]. We have systematically indicated the size of the inpainting domain,
or more precisely the size of a square with the same number of pixels. As can be observed, the
experiments are made for fairly large inpainting domains, which proves the nonlocal nature of
the geometric reconstruction. In Figures 12 and 13, we display examples where the use of a
geometrical guide (obtained using straight lines) improves the reconstruction of the geometry.
In Figure 13(e)–(g), we also display a case of failure in the presence of a T-junction. Indeed,
T-junctions as well as corners are not accounted for in both sketch reconstruction algorithms
presented in section 4.
Figure 14 illustrates that, in some situations, the geometric guiding may yield a result not
better than with the plain “patchwork” algorithm. Note that, for this rectilinear example, it
is preferable to use straight lines rather than Euler spirals for the geometric inpainting. In
Figure 15, we display an example where the geometrically guided method actually yields worse
results than the plain patchwork method. In this example of a dense forest, branches produce
a kind of macrotexture that is kept in the sketch but whose structure is way too complex to
be well interpolated by the geometric step of the inpainting. As a result, the interpolated
sketch is wrong and, instead of helping the reconstruction, deteriorates the result obtained
with only the “patchwork” algorithm.
In Figure 16, we present a toy example made of a textured disk, which illustrates the
possibility of correctly reconstructing such a curvy geometry while reproducing well the texture
part of the image.
In Figures 17 and 18, we show two examples where the use of a geometrical guide that has
been interpolated by means of Euler spirals enables correct inpainting results in difficult cases.
In these relatively high resolution examples, the geometry is restored over large distances. In
both experiments, we also provide the results obtained with Photoshop’s “content aware fill.”
Clearly, for such high-resolution images with a large inpainting domain and a clear long-range
geometric information, our method performs better.
We have reported in Figure 18 the computational time required by our method on a PC
Intel dual core 2 2.66GHz: less than 6 seconds are needed to get the result.
There are other situations where our method does not perform well, or at least worse
than some state-of-the-art inpainting algorithms. This is illustrated in Figure 19, where we
compare the “patchwork” algorithm (second column), the algorithm from [90] implemented
by Yokoya’s group (third column), the method proposed by Kawai, Sato, and Yokoya in [57]
(fourth column), and our method (last column). A few comments on these experiments
can be found in the figure’s caption. Clearly, in these low resolution images, our method
is not competitive if the geometry cannot be correctly computed. Multiscale strategies like
in [90, 57] are much more efficient in this case. However, as illustrated by the performances
of Photoshop’s “content aware fill” in Figures 17 and 18, pure multiscale strategies might be
not sufficient for high resolution images.
For the experiment shown in Figure 20(e), a larger dictionary of patches has been used
in formula (6.1) by simply considering several rotations of each patch. This larger dictionary
permits one to closely follow the reconstructed curves of the sketch even though none of
the patches from the original image has the correct orientation. It is interesting to note
that using geometrically guided rotated patches yields significantly better results than using
rotated patches without guide since, as already mentioned, a richer dictionary of patches
without additional constraints often leads to erratic results. Last, we display in Figure 21 an
example of a successful completion of an electron microscope image illustrating the ability of
our method to restore properly both the texture and the geometry.
Figure 12. (a) original 512 × 768 image; (b) occluded image (occlusion size ∼ 100 × 100); (c) image
restored with the exemplar-based method from [72]; (d) image restored using the method introduced in this
paper, detailed in section 6 (using p = 4, q = 6, λ = 0.5). The geometrical interpolation of the sketch (to obtain
(d)) uses straight lines. A careful examination of the result reveals a few block artifacts that are inherent to the
“patchwork” strategy if no postprocessing is applied in order to remove intensity jumps between blocks. This
could be done using, for instance, the efficient blending method from [71].
8. Conclusion. We have presented a new method for the joint restoration of texture and
geometry. In many situations, and in particular when the missing domain is large, our method
has the ability to properly restore long-range geometric features like edges and, simultaneously,
to sample and paste the texture information correctly. We showed that, in a few high resolution
cases, it clearly outperforms state-of-the-art algorithms. For low resolution images together
with complex geometries, multiscale strategies may be indicated more.
In brief, our approach has the following characteristics:
• It uses a simple and stable sketch representation of the image.
• Broken level lines are completed with Euler spirals that are natural curves for shape
completion. In addition, sharp and possibly crossing discontinuities can be correctly
• The Lagrangian-type variational formulation is flexible enough for the geometric guiding to combine softly with the texture reconstruction.
Figure 13. (a) original 434 × 609 image; (b) occluded image (average occlusion size ∼ 90 × 90); (c) image
restored using the exemplar-based method from [72]; (d) image restored using the method introduced in this
paper, detailed in section 6 (using p = 4, q = 6, λ = 0.5, and straight lines for sketch interpolation). Last line:
example of failure; (e) occluded detail from image (a); (f) reconstructed sketch (using straight lines); (g) image
restored using the geometrical guide. The method is not able to reconstruct the T-junction and therefore yields
• Very few parameters are used in comparison with other inpainting models involving a
geometric guiding.
What are the limitations of our approach? Although it allows us to recover long-range
edges, its performances are somewhat limited when the sketch to be reconstructed is intricate
and contains many edges that can hardly be interpolated without ambiguity. In such a
situation, it appears that a manual intervention is necessary. A natural approach could be to
adapt to the level line framework the contour editing strategy proposed by Elder and Goldberg
Figure 14. A case where the geometrical guiding does not systematically improve the result given by the
“patchwork” algorithm [72]. (a) occluded 1160×800 image (largest occlusion size ∼ 170×170, smallest occlusion
size ∼ 40 × 40); (b) image restored using the exemplar-based method from [72]; (c) image restored using the
method introduced in this paper, detailed in section 6 (using p = 4, q = 6, λ = 0.5, and straight lines for sketch
interpolation); (d) same as (c) but using Euler spirals for the completion of the sketch.
Figure 15. A case where the geometrical guiding yields worse results than the “patchwork” algorithm [72].
In this case, the macrotexture structure is too complex to be correctly interpolated. (a) occluded 676 × 1014
image (occlusion size ∼ 100 × 300); (b) interpolated sketch using straight lines; (c) image restored using the
exemplar-based method from [72]; (d) image restored using the method introduced in this paper, detailed in
section 6 (using p = 4, q = 6, λ = 0.5).
Figure 16. (a) original image (disk diameter ∼ 260); (b) occluded image; (c) image restored using the
“patchwork” algorithm [72]; (d) same restoration process but using for each patch in the dictionary 15 additional
patches obtained by rotation. The result is better but still unsatisfactory; (e) sketch of the image; (f) sketch
reconstructed using Euler spirals; (g) image restored using the method detailed in section 6 (with p = 4, q = 6,
λ = 0.5 and (f) as guiding image); (h) same method as in (g) but with the dictionary enriched by 15 rotations
of each patch so that the disk boundary is smoother.
in [37]. The user could simply mark the pairs of lines to be connected, and the program would
compute the optimal sets of Euler spirals. This would give a more flexible—but no more
automatic—algorithm for inpainting.
We conclude by mentioning that several variants of our model can be proposed: a natural
one consists in pasting samples in priority along the interpolated curves of the sketch, in the
spirit of [80], except that in our case the curves are automatically computed and not drawn
by the user. A second variant consists in changing the energy in (6.1), for instance replacing
the L2 norm by another Lp norm (see [4] for a discussion based on axiomatic requirements).
One can also replace the linear combination of the image and sketch energy terms in (6.1) by
a new energy and define the center of a candidate patch as a solution to
p 1 g
(8.1) y0 = argmin max
IS (yi ) − IS (x0 + yi − y) ,
yi ∈Γq (y)
p 1 ,
I(yi ) − I(x0 + yi − y)
yi ∈Bp,q (y)
x0 +yi −y∈Ac
where Z1 and Z2 are suitable normalization constants depending on the dynamic ranges of ISg
and I, respectively, and on the respective sizes of Γp (·) and Bp,q (·). The choice of Z1 and Z2 is,
however, rather delicate and strongly depends on the type of images, but there are obviously
situations where this energy would be more adapted.
Figure 17. (a) original 900×700 image; (b) occluded image (occlusion size ∼ 230×230); (c) image restored
using the “patchwork” algorithm [72]; (d) image restored using Photoshop’s “content aware fill”; (e) after
inpainting with Euler spirals of the sketch image computed from the luminance channel in a YUV representation
of (b); (f) image restored using the method detailed in section 6 (using (e) and p = 4, q = 6, λ = 0.5).
Appendix. Patches metric and curvature. We briefly investigate a connection between
the exemplar-based approach described in section 6 and the restoration of geometry. It is
obvious from the experiments that exemplar-based inpainting methods restore not only texture
but also the local geometric information. This is not surprising since copying a patch means
copying both the texture and the geometry. However, we may wonder whether a theoretical
link can be found between the replication of patches and the geometry. This is the purpose
of the next proposition (see also [4] for a more general variational formulation), where we
investigate a very simplified model of replication. More precisely, we prove that the difference
between a smooth set and the concatenation of “straight” patches (i.e., intersections between
balls and half-planes) can be evaluated using a criterion that involves the curvature. This is
not surprising since the curvature measures the variation of the tangents to the set boundary.
In addition, this result quantifies the fact (which is obvious from an experimental point of
view) that the more oscillating a boundary is, the more difficult it is to recover it using only
Figure 18. Various experiments with the image of Figure 2. For the purpose of visualization, the results
after processing have been cropped: (a) 450×450 cropped image; (b) occluded image (occlusion size ∼ 90×90); (c)
image restored using the “patchwork” method from [72]; (d) image restored with Photoshop’s “content aware
fill”; (e) inpainting with Euler spirals of the sketch image associated with the luminance channel of a YUV
representation of (b); (f) image restored using the method detailed in section 6 (using (e) and p = 4, q = 6,
λ = 0.5). It takes less than 6 seconds on a PC with Intel dual core 22.66GHz to get this result. More precisely,
the computational times for each step of the algorithm are the following: sketch computation (3”45), geometric
inpainting (0”20), “patchwork” method applied to a region of extra-width 16 pixels around the inpainting region
“straight” patches.
Let E be an open, bounded subset of R2 with C3 boundary. Given x ∈ R2 , we call a
straight patch of size r centered at x any set obtained as the intersection between the disk
Dr (x) of radius r centered at x and a half-plane. We let P denote the collection of all halfplanes in R2 . We want to quantify asymptotically (as r tends to 0) how much E differs at
every point from a straight patch. A natural dissimilarity criterion is defined as
Jr (E) =
( inf (EΔP ) ∩ Dr (x) ) dx,
R2 P ∈P
where Δ is the symmetric difference operator, | · | the Lebesgue measure, and p > 0 a
Figure 19.
An illustration that, for these low-resolution images taken from the database, our method (last column) does not perform better—and may sometimes perform notably worse—than state-of-the-art algorithms as in [90] (third column) or [57] (fourth column).
Results in the second column are obtained with the “patchwork” algorithm. The results in both the third and
the fourth columns are provided by the authors of the database. A possible explanation for the failures of our
method in the second and third lines is that, for such low-resolution images, a good multiscale approach together
with a suitable choice of the block size actually works better than a geometric guiding, especially if the geometry
is complex (third line) or cannot be easily recovered from the level lines due to strong changes of illumination
(second line, where our result could in fact be improved by iterating a Poisson filter).
Proposition A.1. There exists Cp > 0 such that
Jr (E) = Cp r
|κ∂E |p dH1 + o(r 3p+1 ).
Step 1. Up to a translation, we may assume that x = 0 and suppose that, in a neighborhood
of 0, E coincides, up to a rotation, with the set of points (x, y) such that y ≥ λ + κ∂E2(0) x2 for
some λ ∈ R. If λ < −r or λ > r or if κ∂E (0) = 0, then Jr (E, 0) = 0. By symmetry, we can
Figure 20. (a) original 235 × 377 image; (b) occluded image (occlusion size ∼ 40 × 100); (c) image restored
using the “patchwork” algorithm [72]; (d) image restored using our geometry-guided inpainting method based
on Euler spirals (with p = 4, q = 6, and λ = 0.5); (e) same method as in (d), but this time the dictionary
used to performed the exemplar-based step contains patches from the original image as well as rotations of these
now assume that κ := κ∂E (0) > 0 and observe that
κ ∩ Dr (0) .
Jr (E, 0) = inf {y ≥ m + px}Δ y ≥ λ + x2
By symmetry arguments, we observe that for every m ∈ R, p → |({y ≥ m + px}Δ{y ≥
λ + κ2 x2 }) ∩ Dr (0)| isminimal whenever p = 0. Therefore, the computation of Jr (E, 0) reduces
to minimizing m → Dr (0) |λ − m + κ2 x2 | dx; see Figure 22.
Let A be the intersection point between the right branch of the parabola and the circle
Cr (0), and let B be the intersection point with positive x-value between the line y = m and
Figure 21. (a) A low-resolution scanning electron microscope 748 × 1000 image showing an integrated
circuit which was given an overload current and subsequently failed thermally. The fibers are oxides which
formed and grew out of the device during thermal destruction (From: Digital Image Processing, 3rd ed. by R.
C. Gonzalez and R. E. Woods, Prentice Hall, 2008. Used with permission). (b) a mask covering the fibers
(global size ∼ 40 × 775); (c) image restored with the exemplar-based method from [72] (using p = 6, q = 8,
λ = 0.5); (d) image restored using the method detailed in section 6 (using p = 6, q = 8, λ = 0.5, and Euler
spirals for sketch interpolation).
Figure 22. Approximation in Dr (0). Jr (E, 0) is the gray area.
the circle. Clearly, if m minimizes Dr (0) |λ − m + κ2 x2 | dx, then λ ≤ m ≤ yA . Let P be the
patch Dr (0) ∩ {y ≥ m}. Then
xA xB κ 2 κ 2 (A.4)
λ − m + 2 x dx ≤ |EΔP | ≤
λ − m + 2 x dx,
and therefore
xB κ 2 κ 2 λ − m + x dx ≤ Jr (E, 0) ≤ inf
λ − m + x dx.
m∈R −xA m∈R −xB 2
Now the minimum of −xAA |λ − m + κ2 x2 | dx is reached at mA = the median value of λ + κ2 x2
on [−xA , xA ], i.e., mA = λ + κ2 ( 4A ). Analogously, a minimizer of −xBB |λ − m + κ2 x2 | dx is
mB = λ + κ2 (
4 ).
Plugging into (A.5) yields
κ 3
xA ≤ Jr (E, 0) ≤ x3B .
Note that x2B = r 2 − λ2 and x2A = r 2 − λ + κ2 x2A ; thus x2A ≥ r 2 − λ2 − κr 3 , which is positive
for r small enough, and therefore
κ 2
(r − λ2 − κr 3 ) 2 ≤ Jr (E, 0) ≤ (r 2 − λ2 ) 2 .
Step 2. Assume now that E has C3 boundary, and let x ∈ ∂E ⊕ Dr . For r small enough
and after a suitable change of coordinate frames, ∂E on Dr (x) can be approximated as a
parabola of the form λ + κ2 x2 in Dr (0), where κ is the curvature of ∂E at (0, λ) ∈ ∂E in the
new coordinate frame. The approximation error is an o(r 2 ); therefore
κ 2
(r − λ2 − κr 3 ) 2 + o(r 3 ) ≤ Jr (E, 0) ≤ (r 2 − λ2 ) 2 + o(r 3 ).
Step 3. Let dE denote the signed distance function to ∂E (positive outside E, negative
inside). Since dE is smooth in a band containing ∂E and |∇dE | = 1 a.e., we deduce from the
coarea formula that
Jr (E, y) dH1 (y) dλ.
JR (E) =
∂{x: dE (x)≥λ}
By a well-known property of the distance function, dE locally inherits the regularity of ∂E. If r
is small enough, for any x ∈ ∂E⊕Dr , there exists a unique y ∈ ∂E such that x = y+dE (x)n∂E ,
where n∂E denotes the outer normal to E at y. Considering the arc-length s on ∂E, there
exists a parameterization s on ∂{x : dE (x) ≥ λ} such that ds = (1 − κλ) ds. Therefore, by
Fubini’s theorem,
r p
Jr (E, x + λn∂E ) (1 − λκ(x)) dH1 dλ
Jr (E) =
−r ∂Er p
Jr (E, x + λn∂E ) (1 − λκ(x)) dλ dH1 .
By (A.8) and the change of variables λ → λ/r,
(A.10) r
κ p 1 p
(1 − λ2 − κ(x)r)3/2 + o(1) (1 − λ r κ(x)) dλ dH1
∂E 4
κ p
≤ Jr (E) ≤ r
(1 − λ2 )3/2 + o(1) (1 − λ r κ(x)) dλ dH1 .
∂E 4
With the
1 curvature along ∂E being uniformly bounded, the proposition ensues by taking
C = 41p −1 (1 − λ2 )3p/2 dλ.
Acknowledgment. We are grateful to G. Scoutheeten, who implemented a preliminary
version of the code.
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