The quest for optimal sampling: computationally efficient, structure

The quest for optimal sampling: computationally efficient, structure
The quest for optimal sampling:
Computationally efficient, structure-exploiting
measurements for compressed sensing
Ben Adcock, Anders C. Hansen and Bogdan Roman
Abstract An intriguing phenomenon in many instances of compressed sensing is
that the reconstruction quality is governed not just by the overall sparsity of the signal, but also on its structure. This paper is about understanding this phenomenon,
and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.
1 Introduction
Compressed sensing concerns the recovery of signals and images from a small collection of linear measurements. It is now a substantial area of research, accompanied by a mathematical theory that is rapidly reaching a mature state. Applications
of compressed sensing can roughly be divided into two areas. First, type I problems, where the physical device imposes a particular type of measurements. This is
the case in numerous real-world problems, including medical imaging (e.g. Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT)), electron
microscopy, seismic tomography and radar. Second, type II problems, where the
sensing mechanism allows substantial freedom to design the measurements so as to
Ben Adcock
Purdue University, Department of Mathematics, 150 N. University St, West Lafayette, IN 47906,
USA, e-mail: [email protected]
Anders C. Hansen
DAMTP, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd, Cambridge
CB3 0WA, United Kingdom e-mail: [email protected]
Bogdan Roman
DAMTP, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Rd, Cambridge
CB3 0WA, United Kingdom e-mail: [email protected]
Ben Adcock, Anders C. Hansen and Bogdan Roman
improve the reconstructed image or signal. Applications include compressive imaging and fluoresence microscopy.
This paper is devoted to the role of structured sparsity in both classes of problems. It is well known that the standard sparsifying transforms of compressed sensing, i.e. wavelets and their various generalizations, not only give sparse coefficients,
but that there is a distinct structure to this sparsity: wavelet coefficients of natural
signals and images are far more sparse at fine scales than at coarse scales. For type I
problems, recent developments [2, 4] have shown that this structure plays a key role
in the observed reconstruction quality. Moreover, the optimal subsampling strategy
depends crucially on this structure. We recap this work in this paper.
Since structure is vitally important in type I problems, it is natural to ask whether
or not it can be exploited to gain improvements in type II problems. In this paper
we answer this question in the affirmative. We show that appropriately-designed
structured sensing matrices can successfully exploit structure. Doing so leads to
substantial gains over classical approaches in type II problems, based on convex
optimization with universal, random Gaussian or Bernoulli measurements, as well
as more recent structure-exploiting algorithms – such as model-based compressed
sensing [5], TurboAMP [46], and Bayesian compressed sensing [29, 30] – which
incorporate structure using bespoke recovery algorithms. The proposed matrices,
based on appropriately subsampled Fourier/Hadamard transforms, are also computationally efficient
We also review the theory of compressed sensing introduced in [2, 6] with such
structured sensing matrices. The corresponding sensing matrices are highly nonuniversal and do not satisfy a meaningful Restricted Isometry Property (RIP). Yet,
recovery is still possible, and is vastly superior to that obtained from standard RIP
matrices. It transpires that the RIP is highly undesirable if one seeks to exploit structure by designing appropriate measurements. Thus we consider an alternative that
takes such structure into account, known as the RIP in levels [6].
2 Recovery of wavelet coefficients
In many applications of compressed sensing, we are faced with the problem of recovering an image or signal x, considered as a vector in Cn or a function in L2 (Rd ),
that is sparse or compressible in an orthonormal basis of wavelets. If Φ ∈ Cn×n or
Φ ∈ B(L2 (Rd ), `2 (N)) (the set of bounded linear operators) is the corresponding
sparsifying transformation, then we write x = Φc, where c ∈ Cn or c ∈ `2 (N) is the
corresponding sparse or compressible vector of coefficients. Given a sensing operator A ∈ Cm×n or A ∈ B(L2 (Rd ), Cm ) and noisy measurements y = Ax + e with
kek2 ≤ η, the usual approach is to solve the `1 -minimization problem:
min kΦzk1
ky − Azk2 ≤ η.
The quest for optimal sampling
z∈L2 (Rd )
s.t. ky − Azk2 ≤ η.
Throughout this paper, we shall denote a minimizer of (1) or (2) as x̂. Note that
(2) must be discretized in order to be solved numerically, and this can be done by
restricting the minimization to be taken over a finite-dimensional space spanned by
the first n wavelets, where n is taken sufficiently large [1].
As mentioned, compressed sensing problems arising in applications can be divided into two classes:
I. Imposed sensing operators. The operator A is specified by the practical device
and is therefore considered fixed. This is the case in MRI – where A arises by
subsampling the Fourier transform [38, 39] – as well as other examples, including
X-ray CT (see [14] and references therein), radar [31], electron microscopy [7,
35], seismic tomography [37] and radio interferometry [53].
II. Designed sensing operators. The sensing mechanism allows substantial freedom
to design A so as to improve the compressed sensing reconstruction. Some applications belonging to this class are compressive imaging, e.g. the single-pixel
camera [21] and the more recent lensless camera [32], and compressive fluorescence microscopy [47]. In these applications A is assumed to take binary values
(typically {−1, 1}), yet, as we will see later, this is not a significant practical
As stated, the purpose of this paper is to show that insight gained from understanding the application of compressed sensing to type I problems leads to more
effective strategies for type II problems.
2.1 Universal sensing matrices
Let us consider type II problems. In finite dimensions, the traditional compressed
sensing approach has been to construct matrices A possessing the following two
properties. First, they should satisfy the Restricted Isometry Property (RIP). Second,
they should be universal. That is, if Φ ∈ Cn×n is an arbitrary isometry, then AΦ also
satisfies the RIP of the same order as A. Subject to these conditions, a typical result
in compressed sensing is as follows
√ (see [24], for example): if A satisfies the RIP of
order 2k with constant δ2k < 4/ 41 then, for any x ∈ Cn , we have
kx − x̂k2 ≤ C
σk (Φ ∗ x)1
+ Dη,
where x̂ is any minimizer of (1), C and D are positive constants depending only on
δ2k and, for c ∈ Cn ,
σk (c)1 = inf kc − zk1 ,
Σk = {z ∈ Cn : kzk0 ≤ k}.
Ben Adcock, Anders C. Hansen and Bogdan Roman
Hence, x is recovered exactly up to the noise level η and the error σk (c)1 of the
best approximation of c = Φ ∗ x with a k-sparse vector. Since A is universal, one has
complete freedom to choose the sparsifying transformation Φ so as to minimize the
term σk (Φ ∗ x)1 for the particular signal x under consideration.
Typical examples of universal sensing matrices A arise from random ensembles.
In particular, Gaussian or Bernoulli random matrices (with the latter having the
advantage of being binary) both have this property with high probability whenever
m is proportional to k times by a log factor. For this reason, such matrices are often
thought of as ‘optimal’ matrices for compressed sensing.
Remark 1. One significant drawback of random ensembles, however, is that the corresponding matrices are dense and unstructured. Storage and the lack of fast transforms render them impractical for all but small problem sizes. To overcome this, various structured random matrices have also been developed and studied e.g. pseudorandom permutations of columns of Hadamard or DFT (Discrete Fourier Transform)
matrices [25, 32]. Often these admit fast, O (n log n) transforms. However, the best
known theoretical RIP guarantees are usually larger than for (sub)Gaussian random
matrices [24].
2.2 Sparsity structure dependence and the flip test
Since it will become important later, we now describe a quick and simple test, which
we call the flip test, to investigate the presence or absence of an RIP. Success of this
test suggests the existence of an RIP and failure demonstrates its lack.
Let A ∈ Cm×n be a sensing matrix, x ∈ Cn an image and Φ ∈ Cn×n a sparsifying
transformation. Recall that sparsity of the vector c = Φ ∗ x is unaffected by permutations. Thus, let us define the flipped vector
P(c) = c0 ∈ Cn ,
c0i = cn+1−i ,
i = 1, . . . , n,
and using this, we construct the flipped image x0 = Φc0 . Note that, by construction,
we have σk (c)1 = σk (c0 )1 . Now suppose we perform the usual compressed sensing
reconstruction (1) on both x and x0 , giving approximations x̂ ≈ x and x̂0 ≈ x0 . We
now wish to reverse the flipping operation. Thus, we compute x̌ = ΦP(Φ ∗ x̂0 ), which
gives a second approximation to the original image x.
This test provides a simple way to investigate whether or not the RIP holds. To
see why, suppose that A satisfies the RIP. Then by construction, we have that
kx − x̂k2 , kx − x̌k2 ≤ C
σk (Φ ∗ x)1
+ Dη.
Hence both x̂ and x̌ should recover x equally well. In the top row of Figure 1 we
present the result of the flip test for a Gaussian random matrix. As is evident, the
reconstructions x̂ and x̌ are comparable, thus indicating the RIP.
The quest for optimal sampling
Original image
Gauss. to DB4, err=31.54%
Gauss. to Flip DB4, err=31.51%
Subsampling pattern
DFT to DB4, err=10.96%
DFT to Flipped DB4, err=99.3%
Fig. 1: Recovery of an MRI image of a passion fruit using (1) from m = 8192 samples at n =
256×256 resolution (i.e. a 12.5% subsampling rate) with Daubechies-4 wavelets as the sparsifying
transform Φ. Top row: Flip test for Gaussian random measurements. Botton row: Flip test for
subsampled DFT measurements taken according to the subsampling pattern shown in the bottom
left panel. The flip test suggests that the RIP holds for random sensing matrices (top row), but that
there is no RIP for structured sensing matrices with structured sampling (bottom row).
Having considered type II problems, let us now examine the flip test for a type I
problem. As discussed, in applications such as MRI, X-ray CT, radio interferometry,
etc, the matrix A is imposed by the physical sensing device and arises from subsampling the rows of the DFT matrix F ∈ Cn×n .1 Whilst one often has some freedom to
choose which rows to sample (corresponding to selecting particular frequencies at
which to take measurements), one cannot change the matrix F.
It is well known that in order to ensure a good reconstruction, one cannot subsample the DFT uniformly at random (recall that the sparsifying transform is a wavelet
basis), but rather one must sample randomly according to an appropriate nonuni1
In actual fact, the sensing device takes measurements of the continuous Fourier transform of
a function x ∈ L2 (Rd ). As discussed in [1, 4], modelling continuous Fourier measurements as
discrete Fourier measurements can lead to inferior reconstructions, and worse, inverse crimes. To
avoid this, one must consider an infinite-dimensional compressed sensing approach, as in (2). See
[2, 4] for details, as well as [26] for implementation in MRI. However, for simplicity, we shall
continue to work with the finite-dimensional model in the remainder of this paper.
Ben Adcock, Anders C. Hansen and Bogdan Roman
Subsample pattern
TV, err = 9.27% Permuted gradients TV, err = 3.83%
Subsample pattern
TV, err = 28.78% Permuted gradients TV, err = 3.76%
Fig. 2: TV recovery (4) from m = 16384 DFT samples at n = 512 × 512 (6.25% subsampling rate).
The Permuted gradients image was built from the same image gradient vectors as the Original
image, having the same TV norm and gradient sparsity structure, differing only in the ordering
and sign of the gradient vectors. The large error differences confirm that, much like the flip test for
wavelet coefficients, the sparsity structure matters for TV reconstructions as well.
form density [2, 12, 39, 52]. See the bottom left panel of Figure 1 for an example
of a typical density. As can be seen in the next panel, by doing so one achieves a
great recovery. However, the result of the flip test in the bottom right panel clearly
demonstrates that the matrix FΦ does not satisfy an RIP. In particular, the ordering of the wavelet coefficients plays a crucial role in the reconstruction quality. To
explain this, and in particular, the high-quality reconstruction seen in the unflipped
case, one evidently requires a new analytical framework.
Note that the flip test in Figure 1 also highlights another important phenomenon:
namely, the effectiveness of the subsampling strategy depends on the sparsity structure of the image. In particular, two images with the same total sparsity (the original
x and the flipped x0 ) result in wildly different errors when the same sampling pattern is used. Thus we conclude that there is no one optimal sampling strategy for all
sparse vectors of wavelet coefficients.
Let us also note that the same conclusions of the flip test hold when (1) with
wavelets is replaced by TV-norm minimization:
min kzkTV
ky − Azk2 ≤ η.
Recall that kxkTV = ∑i, j k∇x(i, j)k2 , where we have ∇x(i, j) = {D1 x(i, j), D2 x(i, j)},
D1 x(i, j) = x(i + 1, j) − x(i, j), D2 x(i, j) = x(i, j + 1) − x(i, j). In the experiment
leading to Figure 2, we chose an image x ∈ [0, 1]N×N , and then built a different image x0 from the gradient of x so that {k∇x0 (i, j)k2 } is a permutation of {k∇x(i, j)k2 }
for which x0 ∈ [0, 1]N×N . Thus, the two images have the same “TV sparsity” and the
same TV norm. In Figure 2 we demonstrate how the errors differ substantially for
The quest for optimal sampling
the two images when using the same sampling pattern. Note also how the improvement depends both on the TV sparsity structure and on the subsampling pattern.
Analysis of this phenomenon is work in progress. In the remainder of this paper we
will focus on structured sparsity for wavelets.
2.3 Structured sparsity
One of the foundational results of nonlinear approximation is that, for natural images and signals, the best k-term approximation error in a wavelet basis decays
rapidly in k [18, 40]. In other words, wavelet coefficients are approximately ksparse. However, wavelet coefficients possess far more structure than mere sparsity.
Recall that a wavelet basis for L2 (Rd ) is naturally partitioned into dyadic scales.
0 = M0 < M1 < . . . < ∞ be such a partition, and note that Ml+1 − Ml = O 2l in one
dimension and Ml+1 −Ml = O 4l in two dimensions. If x = Φc, let c(l) ∈ CMl −Ml−1
denote the wavelet coefficients of x at scale l = 1, 2, . . ., so that c = (c(1) |c(2) | · · · )> .
Suppose that ε > 0 and define
kl = kl (ε) = min K : ∑ |cπ(i) |2 ≥ ε 2 kc(l) k22 ,
l = 1, 2, . . . ,
where π is a bijection that gives a nonincreasing rearrangement of the entries of
c(l) , i.e. |cπ(i) | ≥ |cπ(i+1) | for i = 1, . . . , Ml − Ml−1 − 1. Sparsity of the whole vector
c means that for large r we have k/n 1, where k = k(ε) = ∑rl=1 kl , is the total
effective sparsity up to finest scale r. However, Figure 3 reveals that not only is this
the case in practice, but we also have so-called asymptotic sparsity. That is
kl /(Ml − Ml−1 ) → 0,
l → ∞.
Put simply, wavelet coefficients are much more sparse at fine scales than they are at
coarse scales.
Note that this observation is by no means new: it is a simple consequence of the
dyadic scaling of wavelets, and is a crucial step towards establishing the nonlinear approximation result mentioned above. However, given that wavelet coefficients
always exhibit such structure, one may ask the following question. For type II problems, are the traditional sensing matrices of compressed sensing – which, as shown
by the flip test (top row of Figure 1), recover all sparse vectors of coefficients equally
well, regardless of ordering – optimal when wavelets are used as the sparsifying
transform? It has been demonstrated in Figure 1 (bottom row) that structure plays a
key role in type I compressed sensing problems. Leveraging this insight, in the next
section we show that significant gains are possible for type II problems in terms
of both the reconstruction quality and computational efficiency when the sensing
matrix A is designed specifically to take advantage of such inherent structure.
Ben Adcock, Anders C. Hansen and Bogdan Roman
Sparsity, kl (ǫ)/(Ml − Ml−1 )
Level 1
Level 2
Level 3
Level 4
Level 5
Level 6
Level 7
Worst sparsity
Best sparsity
Relative threshold, ǫ
Fig. 3: Left: GLPU phantom [27]. Right: Relative sparsity of Daubechies-4 coefficients. Here the
levels correspond to wavelet scales and kl (ε) is given by (5). Each curve shows the relative sparsity
at level l as a function of ε. The decreasing nature of the curves for increasing l confirms (6).
Remark 2. Asymptotic sparsity is by no means limited to wavelets. A similar property holds for other types of -lets, including curvelets [9, 10], contourlets [19, 42] or
shearlets [16, 17, 34] (see [4] for examples of Figure 3 based on these transforms).
More generally, any sparsifying transform that arises (possibly via discretization)
from a countable basis or frame will typically exhibit asymptotic sparsity.
Remark 3. Wavelet coefficients (and their various generalizations) actually possess
far more structure than the asymptotic sparsity (6). Specifically, they tend to live
on rooted, connected trees [15]. There are a number of existing algorithms which
seek to exploit such structure within a compressed sensing context. We shall discuss
these further in Section 5.
3 Efficient sensing matrices for structured sparsity
For simplicity, we now consider the finite-dimensional setting, although the arguments extend to the infinite-dimensional case [2]. Suppose that Φ ∈ Cn×n corresponds to a wavelet basis so that c = Φ ∗ x is not just k-sparse, but also asymptotically sparse with the sparsities k1 , . . . , kr within the wavelet scales being known. As
before, write c(l) for set of coefficients at scale l. Considering type II problems, we
now seek a sensing matrix A with as few rows m as possible that exploits this local
sparsity information.
3.1 Block-diagonality and structured Fourier/Hadamard sampling
For the l th level, suppose that we assign a total of ml ∈ N rows of A in order to recover the kl nonzero coefficients of c(l) . Note that m = ∑rl=1 ml . Consider the product
B = AΦ of the sensing matrix A and the sparsifying transform Φ. Then there is a
The quest for optimal sampling
natural decomposition of B into blocks {B jl }rj,l=1 of size m j × (Ml − Ml−1 ), where
each block corresponds to the m j measurements of the Ml − Ml−1 wavelet functions
at the l th scale.
Suppose it were possible to construct a sensing matrix A such that (i) B was
block diagonal, i.e. B jl = 0 for j 6= l, and (ii) the diagonal blocks Bll satisfied an
RIP of order 2kl whenever ml was proportional to kl times by the usual log factor.
In this case, one recovers the coefficients c(l) at scale l from near-optimal numbers
of measurements using the usual reconstruction (1).
This approach, originally proposed by Donoho [20] and Tsaig & Donoho [48]
under the name of ‘multiscale compressed sensing’, allows for structure to be exploited within compressed sensing. Similar ideas were also pursued by Romberg
[44] within the context of compressive imaging. Unfortunately, it is normally impossible to design an m × N matrix A such that B = AΦ is exactly block diagonal.
Nevertheless, the notion of block-diagonality provides insight into better designs
for A than purely random ensembles. To proceed, we relax the requirement of strict
block-diagonality, and instead ask whether there exist practical sensing matrices A
for which B is approximately block-diagonal whenever the sparsifying transform
Φ corresponds to wavelets. Fortunately, the answer to this question is affirmative:
as we shall explain next, and later confirm with a theorem, approximate blockdiagonality can be ensured whenever A arises by appropriately subsampling the
rows of the Fourier or Hadamard transform. Recalling that the former arises naturally in type I problems, this points towards the previously-claimed conclusion
that new insight brought about by studying imposed sensing matrices leads to better
approaches for the type II problem.
Let F ∈ Cn×n be either the discrete Fourier or discrete Hadamard transform. Let
Ω ⊆ {1, . . . , n} be an index set of size |Ω | = m. We now consider choices for A of
the form A = PΩ F, where PΩ ∈ Cm×n is the restriction operator that selects rows of
F whose indices lie in Ω . We seek an Ω that gives the desired block-diagonality. To
do this, it is natural to divide up Ω itself into r disjoint blocks
Ω = Ω1 ∪ · · · ∪ Ωr ,
|Ωl | = ml ,
where the l th block Ωl ⊆ {Nl−1 , . . . , Nl } corresponds to the ml samples required to
recover the kl nonzero coefficients at scale l. Here the parameters 0 = N0 < N1 <
. . . < Nr = n are appropriately chosen and delineate frequency bands from which
the ml samples are taken.
In Section 3.3, we explain why this choice of A works, and in particular, how to
choose the sampling blocks Ωl . In order to do this, it is first necessary to recall the
notion of incoherent bases.
Ben Adcock, Anders C. Hansen and Bogdan Roman
3.2 Incoherent bases and compressed sensing
Besides random ensembles, a common approach in compressed sensing is to design
sensing matrices using orthonormal systems that are incoherent with the particular
choice of sparsifying basis Φ [11, 12, 24]. Let Ψ ∈ Cn×n be an orthonormal basis
of Cn . The (mutual) coherence of Φ and Ψ is the quantity
µ = µ(Ψ ∗ Φ) = max |(Ψ ∗ Φ)i, j |2 .
i, j=1,...,n
We say Ψ and Φ are incoherent if µ(Ψ , Φ) ≤ a/n for some a ≥ 1 independent of n.
Given such a Ψ , one constructs the sensing matrix A = PΩ Ψ , where Ω ⊆ {1, . . . , N},
|Ω | = m is chosen uniformly at random. A standard result gives that a k-sparse signal
x in the basis Φ is recovered exactly with probability at least 1 − p, provided
m ≥ Cka log(1 + p−1 ) log(n),
for some universal constant C > 0 [24]. As an example, consider the Fourier basis
Ψ = F. This is incoherent with the canonical basis Φ = I with optimally-small constant a = 1. Fourier matrices subsampled uniformly at random are efficient sensing
matrices for signals that are themselves sparse.
However, the Fourier matrix is not incoherent with a wavelet basis: µ(F, Φ) =
O (1) as n → ∞ for any orthonormal wavelet basis [2]. Nevertheless, Fourier samples
taken within appropriate frequency bands (i.e. not uniformly at random) are locally
incoherent with wavelets in the corresponding scales. This observation, which we
demonstrate next, explains the success of the sensing matrix A constructed in the
previous subsection for an appropriate choice of Ω1 , . . . , Ωr .
3.3 Local incoherence and near block-diagonality of Fourier
measurements with wavelets
For expository purposes, we consider the case of one-dimensional Haar wavelets.
We note however that the arguments generalize to arbitrary compactly-supported
orthonormal wavelets, and to the infinite-dimensional setting where the unknown
image x is a function. See Section 4.3.
Let j = 0, . . . , r − 1 (for convenience we now index from 0 to r − 1, as opposed
to 1 to r) be the scale and p = 0, . . . , 2 j − 1 the translation. The Haar basis consists
of the functions {ψ} ∪ {φ j,p }, where ψ ∈ Cn is the normalized scaling function
and φ j,p are the scaled and shifted versions of the mother wavelet φ ∈ Cn . It is a
straightforward, albeit tedious, exercise to show that
|F φl,p (ω)| = 2l/2−r+1
l+1 )|2
| sin(πω/2l+1 )|2
l/2 | sin(πω/2
| sin(πω/2r )|
|ω| < 2r ,
The quest for optimal sampling
where F denotes the DFT [3]. This suggests that the Fourier transform F φl,p (ω) is
large when ω ≈ 2l , yet smaller when ω ≈ 2 j with j 6= l. Hence we should separate
frequency space into bands of size roughly 2 j .
Let F ∈ Cn×n be the DFT matrix with rows indexed from −n/2 + 1 to n/2.
Following an approach of [12], we now divide these rows into the following disjoint
frequency bands
W0 = {0, 1},
W j = {−2 j + 1, . . . , −2 j−1 } ∪ {2 j−1 + 1, . . . , 2 j },
l = 0, . . . , r − 1.
With this to hand, we now define Ω j to be a subset of W j of size |Ω j | = m j chosen
uniformly at random. Thus, the overall sensing matrix A = PΩ F takes measurements
of the signal x by randomly drawing m j samples its Fourier transform within the
frequency bands W j .
Having specified Ω , let us note that the matrix U = FΦ naturally divides into
blocks, with the rows corresponding to the frequency bands Wl and the columns
2 j ×2l . For
corresponding to the wavelet scales. Write U = {U jl }r−1
j,l=0 where U jl ∈ C
compressed sensing to succeed in this setting, we require two properties. First, the
diagonal blocks U j j should be incoherent, i.e. µ(U j j ) = O 2− j . Second, the coherences µ(U jl ) of the off-diagonal blocks U jl should be appropriately small in comparison to µ(U j j ). These two properties are demonstrated in the following lemma:
Lemma 1. We have µ(U j j ) . 2− j and, in general,
µ(U jl ) . µ(U j j )2−| j−l| ,
j, l = 0, . . . , r − 1
Hence U is approximately block diagonal, with exponential decay away from the
diagonal blocks. Fourier measurements subsampled according to the above strategy
are therefore ideally suited to recover structured sparse wavelet coefficients.2
The left panel of Figure 4 exhibits this decay by plotting the absolute values of
the matrix U. In the right panel, we also show a similar result when the Fourier
transform is replaced by the Hadamard transform. This is an important case, since
the measurement matrix is binary. The middle panel of the figure shows the U matrix
when Legendre polynomials are used as the sparsifying transform, as is sometimes
the case for smooth signals. It demonstrates that diagonally-dominated coherence is
not just a phenomenon associated with wavelets.
Having identified a measurement matrix to exploit structured sparsity, let us
demonstrate its effectiveness. In Figure 5 we compare these measurements with the
case of random Bernoulli measurements (this choice was made over random Gaussian measurements because of storage issues). As is evident, at all resolutions we
see a significant advantage, since the former strategy exploits the structured sparsity.
Note that for both approaches, the reconstruction quality is resolution dependent: the
error decreases as the resolution increases, due to the increasing sparsity of wavelet
coefficients at higher resolutions. However, because the Fourier/wavelets matrix U
For brevity, we do not give the proof of this lemma or the later recovery results for Haar wavelets,
Theorem 2. Details of the proof can be found in the short note [3].
Ben Adcock, Anders C. Hansen and Bogdan Roman
Fig. 4: The absolute values of the matrix U = FΦ, where F is the discrete Fourier transform (left
and middle) or the Hadamard transform (right) and the sparsifying transform Φ corresponds to
Haar wavelets (left and right) or Legendre polynomials (middle). Light colours correspond to large
values and dark colours to small values.
256×256, err = 41.6%
512×512, err = 25.3%
1024×1024, err = 11.6%
256×256, err = 21.9%
512×512, err = 10.9%
1024×1024, err = 3.1%
Fig. 5: Recovery from 12.5% measurements using (1) with Daubechies-4 wavelets as the sparsifying transform. Top row: Random Bernoulli sensing matrix. Bottom row: Fourier sensing matrix
with multilevel subsampling (see Definition 3). All images are 256×256 crops of the original full
resolution versions in order to aid the visual comparison.
is asymptotically incoherent (see also Section 4.1), it exploits the inherent asymptotic sparsity structure (6) of the wavelet coefficients as the resolution increases, and
thus gives successively greater improvements over random Bernoulli measurements.
The quest for optimal sampling
Remark 4. Besides improved reconstructions, an important feature of this approach
is storage and computational time. Since F and Φ have fast, O (n log n), transforms,
the matrix A = PΩ FΦ does not need to be stored, and the reconstruction (1) can be
performed efficiently with standard `1 solvers (we use SPGL1 [49, 50] throughout).
Recall that in type I problems such as MRI, we are constrained by the physics
of the device to take Fourier measurements. A rather strange conclusion of Figure
5 is the following: compressed sensing actually works better for MRI with the intrinsic measurements, than if one were able to take optimal (in the sense of the standard sparsity-based theory) random (sub)Gaussian measurements. This has practical
consequences. In MRI there is actually a little flexibility to design measurements,
based on specifying appropriate pulses. By doing this, a number of approaches
[28, 36, 43, 45, 54] have been proposed to make MRI measurements closer to uniformly incoherent with wavelets (i.e. similar to random Gaussians). On the other
hand, Figure 5 suggests that one can obtain great results in practice by appropriately
subampling the unmodified Fourier operator.
4 A general framework for compressed sensing based on
structured sparsity
Having argued for the particular case of Fourier samples with Haar wavelets, we
now describe a general mathematical framework for structured sparsity. This is
based on work in [2].
4.1 Concepts
We shall work in both the finite- and infinite-dimensional settings, where U ∈ Cn×n
or U ∈ B(`2 (N)) respectively. We assume throughout that U is an isometry. This
occurs for example when U = Ψ ∗ Φ for an orthonormal basis Ψ and an orthonormal
system Φ, as is the case for the example studied in the previous section: namely,
Fourier sampling and a wavelet sparsifying basis. However, framework we present
now is valid for an arbitrary isometry U, not just this particular example. We discuss
this case further in Section 4.3.
We first require the following definitions. In the previous section it was suggested
to divide both the sampling strategy and the sparse vector of coefficients into disjoint
blocks. We now formalize these notions:
Definition 1 (Sparsity in levels). Let c be an element of either CN or `2 (N). For
r ∈ N let M = (M1 , . . . , Mr ) ∈ Nr with 1 ≤ M1 < . . . < Mr and k = (k1 , . . . , kr ) ∈ Nr ,
with k j ≤ M j − M j−1 , k = 1, . . . , r, where M0 = 0. We say that c is (k, M)-sparse if,
for each j = 1, . . . , r, the set
Ben Adcock, Anders C. Hansen and Bogdan Roman
∆ j := supp(c) ∩ {M j−1 + 1, . . . , M j },
satisfies |∆ j | ≤ k j . We denote the set of (k, M)-sparse vectors by Σk,M .
This definition allows for differing amounts of sparsity of the vector c in different
levels. Note that the levels M do not necessarily correspond to wavelet scales – for
now, we consider a general setting. We also need a notion of best approximation:
Definition 2 ((k, M)-term approximation). Let c be an element of either CN or
`2 (N). We say that c is (k, M)-compressible if σk,M (c) is small, where
σk,M (c)1 = inf kc − zk1 .
As we have already seen for wavelet coefficients, it is often the case that k j /(M j −
M j−1 ) → 0 as j → ∞. In this case, we say that c is asymptotically sparse in levels.
However, we stress that this framework does not explicitly require such decay.
We now consider the level-based sampling strategy:
Definition 3 (Multilevel random sampling). Let r ∈ N, N = (N1 , . . . , Nr ) ∈ Nr with
1 ≤ N1 < . . . < Nr , m = (m1 , . . . , mr ) ∈ Nr , with m j ≤ N j − N j−1 , j = 1, . . . , r, and
suppose that
Ω j ⊆ {N j−1 + 1, . . . , N j },
|Ω j | = m j ,
j = 1, . . . , r,
are chosen uniformly at random, where N0 = 0. We refer to the set
Ω = ΩN,m = Ω1 ∪ . . . ∪ Ωr .
as an (N, m)-multilevel sampling scheme.
As discussed in Section 3.2, the (infinite) Fourier/wavelets matrix U = FΦ is
globally coherent. However, as shown in Lemma 1, the coherence of its ( j, l)th block
is much smaller. We therefore require a notion of local coherence:
Definition 4 (Local coherence). Let U be an isometry of either CN or `2 (N). If
N = (N1 , . . . , Nr ) ∈ Nr and M = (M1 , . . . , Mr ) ∈ Nr with 1 ≤ N1 < . . . Nr and 1 ≤
M1 < . . . < Mr the ( j, l)th local coherence of U with respect to N and M is given by
µN,M ( j, l) =
µ(PN jj−1 UPMll−1 )µ(PN jj−1 U),
k, l = 1, . . . , r,
where N0 = M0 = 0 and Pba denotes the projection matrix corresponding to indices
{a + 1, . . . , b}. In the case where U is an operator on `2 (N), we also define
µN,M ( j, ∞) =
µ(PN jj−1 UPM⊥r−1 )µ(PN jj−1 U),
j = 1, . . . , r.
The quest for optimal sampling
Note that the local coherence µN,M ( j, l) is not just the coherence µ(PN jj−1 UPMll−1 )
in the ( j, l)th block. For technical reasons, one requires the product of this and the
coherence µ(PN jj−1 U) in the whole jth row block.
Remark 5. In [33], the authors define a local coherence of a matrix U in the jth
row (as opposed to row block) to be the maximum of its entries in that row. Using
this, they prove recovery guarantees for the Fourier/Haar wavelets problem based
on the RIP and the global sparsity k. Unfortunately, these results do not explain the
importance of structured sparsity as shown by the flip test. Conversely, our notion of
local coherence also takes into account the sparsity levels. As we will see in Section
4.2, this allows one to establish recovery guarantees that are consistent with the flip
test and properly explain the role of structure in the reconstruction.
Recall that in practice (see Figure 4), the local coherence often decays along the
diagonal blocks and in the off-diagonal blocks. Loosely speaking, we say that the
matrix U is asymptotically incoherent in this case.
In Section 3.3 we argued that the Fourier/wavelets matrix was nearly blockdiagonal. In our theorems, we need to account for the off-diagonal terms. To do
this in the general setup, we require a notion of a relative sparsity:
Definition 5 (Relative sparsity). Let U be an isometry of either CN or `2 (N). For
N = (N1 , . . . , Nr ) ∈ Nr , M = (M1 , . . . , Mr ) ∈ Nr with 1 ≤ N1 < . . . < Nr and 1 ≤ M1 <
. . . < Mr , k = (k1 , . . . , kr ) ∈ Nr and j = 1, . . . , r, the jth relative sparsity is given by
K j = K j (N, M, k) =
z∈Σk,M ,kηk∞ ≤1
kPN jj−1 Uzk2 ,
where N0 = M0 = 0.
The relative sparsities K j take into account interferences between different sparsity level caused by the non-block diagonality of U.
4.2 Main theorem
Given the matrix/operator U and a multilevel sampling scheme Ω , we now consider
the solution of the convex optimization problem
min kzk1
s.t. kPΩ y − PΩ Uzk2 ≤ η,
where y = Uc + e, kek2 ≤ η. Note that if U = Ψ ∗ Φ, x = Φc is the signal we wish
to recover and ĉ is a minimizer of (8) then this gives the approximation x̂ = Φ ĉ to x.
Theorem 1. Let U ∈ CN×N be an isometry and c ∈ CN . Suppose that Ω = ΩN,m
is a multilevel sampling scheme, where N = (N1 , . . . , Nr ) ∈ Nr , Nr = n, and m =
(m1 , . . . , mr ) ∈ Nr . Let ε ∈ (0, e−1 ] and suppose that (k, M), where M = (M1 , . . . , Mr ) ∈
Nr , Mr = n, and k = (k1 , . . . , kr ) ∈ Nr , are any pair such that the following holds:
Ben Adcock, Anders C. Hansen and Bogdan Roman
(i) We have
N j − N j−1
log(ε −1 )
∑ µN,M ( j, l)kl
log (n) ,
j = 1, . . . , r.
(ii) We have m j & m̂ j log(ε −1 ) log (n), where m̂ j is such that
r N j − N j−1
− 1 µN,M ( j, l)k̃ j , l = 1, . . . , r,
1& ∑
m̂ j
for all k̃1 , . . . , k̃r ∈ (0, ∞) satisfying
k̃1 + . . . + k̃r ≤ k1 + . . . + kr ,
k̃ j ≤ K j (N, M, k).
Suppose that ĉ ∈ CN is a minimizer of (8). Then, with probability exceeding 1 − kε,
where k = k1 + . . . + kr , we have that
√ √ (11)
kc − ĉk2 ≤ C η D 1 + E k + σk,M (c)1 ,
log2 (6ε −1 )
log2 (4En s)
for some constant C, where σk,M (c)1 is as in (7), D = 1 +
and E =
max j=1,...,r {(N j − N j−1 )/m j }. If m j = N j − N j−1 , j = 1, . . . , r, then this holds with
probability 1.
A similar theorem can be stated and proved in the infinite-dimensional setting
[2]. For brevity, we shall not do this.
The key feature of Theorem 1 is that the bounds (9) and (10) involve only local
quantities: namely, local sparsities k j , local coherences µ( j, l), local measurements
m j and relative sparsities K j . Note that the estimate (11) directly generalizes a standard compressed sensing estimate for sampling with incoherent bases [1, 11] to the
case of multiple levels. Having said this, it is not immediately obvious how to understand these bounds in terms of how many measurements m j are actually required
in the jth level. However, it can be shown that these bounds are in fact sharp for a
large class of matrices U [2]. Thus, little improvement is possible. Moreover, in the
important case Fourier/wavelets, one can analyze the local coherences µ( j, l) and
relative sparsities K j to get such explicit estimates. We consider this next.
4.3 The case of Fourier sampling with wavelets
Let us consider the example of Section 3.3, where the matrix U arises from the
Fourier/Haar wavelet pair, the sampling levels are correspond to the aforementioned
frequency bands W j and the sparsity levels are the Haar wavelet scales.
The quest for optimal sampling
Theorem 2. 3 Let U and Ω be as in Section 3.3 (recall that we index over j, l =
0, . . . , r − 1) and suppose that x ∈ Cn . Let ε ∈ (0, e−1 ] and suppose that
| j−l|
m j & k j + ∑ 2− 2 kl  log(ε −1 ) log2 (n),
j = 0, . . . , r − 1.
l6= j
Then, with probability exceeding 1 − kε, where k = k0 + . . . + kr−1 , any minimizer x̂
of (1) satisfies
kx − x̂k2 ≤ C η D(1 + E k) + σk,M (Φ ∗ x)1 ,
σk,M (Φ ∗ x)1
is as in (7), D = 1 +
log2 (6ε −1 )
log2 (4En k)
and E = max j=0,...,r−1 {(N j −
N j−1 )/m j }. If m j = |W j |, j = 0, . . . , r − 1, then this holds with probability 1.
The key part of this theorem is (12). Recall that if U were exactly block diagonal,
then m j & k j would suffice (up to log factors). The estimate (12) asserts that we
require only slightly more samples, and this is due to interferences from the other
sparsity levels. However, as | j − l| increases, the effect of these levels decreases
exponentially. Thus, the number of measurements m j required in the jth frequency
band is determined predominantly by the sparsities in the scales l ≈ j. Note that kl ≈
O (k j ) when l ≈ j for typical signals and images, so the estimate (12) is typically on
the order of k j in practice.
The estimate (12) both agrees with the conclusion of the flip test in Figure 1
and explains the results seen. Flipping the wavelet coefficients changes the local
sparsities k1 , . . . , kr . Therefore to recover the flipped image to the same accuracy as
the unflipped image, (12) asserts that one must change the local numbers of measurements m j . But in Figure 1 the same sampling pattern was used in both cases,
thereby leading to the worse reconstruction in the flipped case. Note that (12) also
highlights why the optimal sampling pattern must depend on the image, and specifically, the local sparsities. In particular, there can be no optimal sampling strategy
for all images.
Note that Theorem 2 is a simplified version, presented here for the purposes of
elucidation, of a more general result found in [2] which applies to all compactlysupported orthonormal wavelets in the infinite-dimensional setting.
5 Structured sampling and structured recovery
Structured sparsity within the context of compressed sensing has been considered
in numerous previous works. See [5, 8, 13, 20, 22, 23, 29, 30, 41, 48, 51, 46] and
For a proof, we refer to [3].
Ben Adcock, Anders C. Hansen and Bogdan Roman
Rand. Gauss to DB4, ModelCS, err = 41.8%
Rand. Gauss to DB4, TurboAMP, err = 39.3%
Rand. Gauss to DB4, BCS, err = 29.6%
Multilevel DFT to DB4, `1 , err = 18.2%
Fig. 6: Recovery from 12.5% measurements at 256×256. Comparison between random sampling
with structured recovery and structured sampling with `1 -minimization recovery.
references therein. For the problem of reconstructing wavelet coefficients, most efforts have focused on their inherent tree structure (see Remark 3). Three well-known
algorithmic approaches for doing this are model-based compressed sensing [5], TurboAMP [46], and Bayesian compressed sensing [29, 30]. All methods use Gaussian
or Bernoulli random measurements, and seek to leverage the wavelet tree structure – the former deterministically, the latter two in a probabilistic manner – by
appropriately-designed recovery algorithms (based on modifications of existing iterative algorithms for compressed sensing). In other words, structure is incorporated
solely in the recovery algorithm, and not in the measurements themselves.
The quest for optimal sampling
Subsampling pattern
DFT to DB4,
error = 10.95%
DFT to Levels Flipped DB4,
error = 12.28%
Fig. 7: The flip test in levels for the image considered in Figure 1.
In Figure 6 we compare these algorithms with the previously-described method
of multilevel Fourier sampling (similar results are also witnessed with the Hadamard
matrix). Note that the latter, unlike other three methods, exploits structure by taking
appropriate measurements, and uses an unmodified compressed sensing algorithm
(`1 minimization). As is evident, this approach is able to better exploit the sparsity structure, leading to a significantly improved reconstruction. This experiment
is representative of a large set tested. In all cases, we find that exploiting structure
by sampling with asymptotically incoherent Fourier/Hadamard bases outperforms
such approaches that seek to leverage structure in the recovery algorithm.
6 The Restricted Isometry Property in levels
The flip test demonstrates that the subsampled Fourier/wavelets matrix PΩ U does
not satisfy a meaningful RIP. However, due to the sparsity structure of the signal,
we are still able to reconstruct, as was confirmed in Theorem 1. The RIP is therefore too crude a tool to explain the recoverability properties of structured sensing
matrices. Having said that, the RIP is a useful for deriving uniform, as opposed
to nonuniform, recovery results, and for analyzing other compressed sensing algorithms besides convex optimization, such as greedy methods [24]. This raises the
question of whether there are alternatives which are satisfied by such matrices. One
possibility which we now discuss is the RIP in levels. Throughout this section we
shall work in the finite-dimensional setting. For proofs of the theorems, see [6].
Definition 6. Given an r-level sparsity pattern (k, M), where Mr = n, we say that
the matrix A ∈ Cm×n satisfies the RIP in levels (RIPL ) with RIPL constant δk ≥ 0 if
for all x in Σk,M we have
(1 − δk )kxk22 ≤ kAxk22 ≤ (1 + δk )kxk22 .
Ben Adcock, Anders C. Hansen and Bogdan Roman
The motivation for this definition is the following. Suppose that we repeat the flip
test from Figure 1 except that instead of completely flipping the coefficients we only
flip them within levels corresponding to the wavelet scales. We will refer to this as
the flip test in levels. Note the difference between Figure 1 and Figure 7, where
latter presents the flip test in levels: clearly, flipping within scales does not alter
the reconstruction quality. In light of this experiment, we propose the above RIP in
levels definition so as to respect the level structure.
We now consider the recovery properties of matrices satisfying the RIP in levels.
For this, we define the ratio constant λk,M of a sparsity pattern (k, M) to be λk,M :=
max j,l k j /kl . We assume throughout that k j ≥ 1, ∀ j, so that ηk,M < ∞, and also that
Mr = n. We now have the following:
Theorem 3. Let (k, M) be a sparsity pattern with r levels and ratio constant λ =
λk,M . Suppose that the matrix A has RIPL constant δ2k satisfying
δ2k < q √
r( λ + 1/4)2 + 1
Let x ∈ Cn , y ∈ Cm be such that kUx − yk2 ≤ η, and let x̂ be a minimizer of
min kzk1
ky − Azk2 ≤ η.
kx − x̂k1 ≤ Cσk,M (x)1 + D kη,
where k = k1 + . . . + kr and the constants C and D depend only on δ2k .
This theorem is a generalization of a known result in standard (i.e. one-level)
sensing. Note that (13) reduces to the well-known estimate δ2k ≤
4/ 41 [24] when r = 1. On the other hand, in the multiple level case the reader
may be concerned that the bound ceases to be useful, since the right-hand side of
(13) deteriorates with both the number of levels r and the sparsity ratio λ . As we
show in the following two theorems, the dependence on r and λ in (13) is sharp:
Theorem 4. Fix a ∈ N. There exists a matrix A with two levels and a sparsity pattern
(k, M) such that the RIPL constant δak and ratio constant λ = λk,M satisfy
δak ≤ 1/| f (λ )|,
where f (λ ) = o( λ ), but there is an x ∈ Σk,M such that x is not the minimizer of
min kzk1
Az = Ax.
Roughly speaking, Theorem 4 says that if we fix the number of levels and try to
replace (13) with a condition of the form
δ2k < √ λ − 2
C r
The quest for optimal sampling
for some constant C and some α < 1 then the conclusion of Theorem 3 ceases to
hold. In particular, the requirement on δ2k cannot be independent of λ . The parameter a in the statement of Theorem 4 also means that we cannot simply fix the issue
by changing δ2k to δ3k , or any further multiple of k.
Similarly, we also have a theorem that shows that the dependence on the number
of levels r cannot be ignored.
Theorem 5. Fix a ∈ N. There exists a matrix A and an r-level sparsity pattern (k, M)
with ratio constant λk,M = 1 such that the RIPL constant δak satisfies
δak ≤ 1/| f (r)|,
where f (r) = o( r), but there is an x ∈ Σk,M such that x is not the minimizer of
min kzk1
Az = Ax.
These two theorems suggests that, at the level of generality of the RIPL , one must
accept a bound that deteriorates with the number of levels and ratio constant. This
begs the question: what is the effect of such deterioration? To understand this, consider the case studied earlier, where the r levels correspond to wavelet scales. For
typical images, the ratio constant λ grows only very mildly with n, where n = 2r is
the dimension. Conversely, the number of levels is equal to log2 (n). This suggests
that estimates for the Fourier/wavelet matrix that ensure an RIP in levels (thus guaranteeing uniform recovery) will involve at least several additional factors of log(n)
beyond what is sufficient for nonuniform recovery (see Theorem 2). Proving such
estimates for the Fourier/wavelets matrix is work in progress.
Acknowledgements The authors thank Andy Ellison from Boston University Medical School for
kindly providing the MRI fruit image, and General Electric Healthcare for kindly providing the
brain MRI image. BA acknowledges support from the NSF DMS grant 1318894. ACH acknowledges support from a Royal Society University Research Fellowship. ACH and BR acknowledge
the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L003457/1.
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