1 Image Formation
Optical Components of the Eye . . . . . . . . . . . . . . . . . . . . . . .
Reflections From the Eye . . . . . . . . . . . . . . . . . . . . . . . . . . .
Linear Systems Methods . . . . . . . . . . . . . . . . . . . . . . . . . . .
Shift-Invariant Linear Transformations . . . . . . . . . . . . . . . . . . .
The Optical Quality of the Eye . . . . . . . . . . . . . . . . . . . . . . . .
Lenses, Diffraction and Aberrations . . . . . . . . . . . . . . . . . . . .
List of Figures
EyeBall From Salzman . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Monitor to Retina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ophtalmoscope Principles . . . . . . . . . . . . . . . . . . . . . . . . . .
Double Pass Instrument . . . . . . . . . . . . . . . . . . . . . . . . . . .
Retinal Cross Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Campbell and Gubisch Data . . . . . . . . . . . . . . . . . . . . . . . . .
Homogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Superposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Homogeneity and Superposition . . . . . . . . . . . . . . . . . . . . . .
1.10 Matrix Multiplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.11 Sinusoids and Double Passage . . . . . . . . . . . . . . . . . . . . . . .
1.12 The Estimated Linespread Function . . . . . . . . . . . . . . . . . . . .
1.13 Westheimer’s Linespread Function . . . . . . . . . . . . . . . . . . . . .
1.14 Example Retinal Images . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.15 The MTF for Westheimer’s Linespread . . . . . . . . . . . . . . . . . . .
1.16 Snell’s Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.17 Depth of Field in the Human Eye . . . . . . . . . . . . . . . . . . . . . .
1.18 Pinhole Optics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.19 Diffraction limited pinhole image . . . . . . . . . . . . . . . . . . . . . .
1.20 Diffraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.21 Pointspread Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.22 Astigmatism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.23 Chromatic Aberration . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.24 OTF of Chromatic Aberration . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 1
Image Formation
The cornea and lens are at the interface between the physical world of light and the
neural encoding of the visual pathways. The cornea and lens bring light into focus at
the light sensitive receptors in our retina and initiate a series of visual events that
result in our visual experience.
The initial encoding of light at the retina is but the first in a series of visual
transformations: The stimulus incident at the cornea is transformed into an image at
the retina. The retinal image is transformed into a neural response by the light
sensitive elements of the eye, the photoreceptors. The photoreceptor responses are
transformed to a neural response on the optic nerve. The optic nerve representation
is transformed into a cortical representation, and so forth. We can describe most of
our understanding of these transformations, and thus most of our understanding of
the early encoding of light by the visual pathways by using linear systems theory.
Because all of our visual experience is limited by the image formation within our
eye, we begin by describing this transformation of the light signal and we will use
this analysis as an introduction to linear methods.
1.1 Optical Components of the Eye
Figure 1.1 contains an overview of the imaging components of the eye. Light from a
source arrives at the cornea and is focused by the cornea and lens onto the
photoreceptors, a collection of light sensitive neurons. The photoreceptors are part of a
thin layer of neural tissue, called the retina. The photoreceptor signals are
communicated through the several layers of retinal neurons to the neurons whose
output fibers makes up the optic nerve. The optic nerve fibers exit through a hole in
the retina called the optic disk. The optical imaging of light incident at the cornea into
an image at the retinal photoreceptors is the first visual transformation. Since all of
Optic nerve
Figure 1.1: The imaging components of the eye. The cornea and lens focus the image onto
the retina. Light enters through the pupil which is bordered by the iris. The fovea is a
region of the retina that is specialized for high visual acuity and color perception. The
retinal output fibers leave at a point in the retina called the blindspot. The bundle of
output fibers is called the optic nerve.
our visual experiences are influenced by this transformation, we begin the study of
vision by analyzing the properties of image formation.
When we study transformations, we must specify their inputs and outputs. As an
example, we will consider how simple one-dimensional intensity patterns displayed
on a video display monitor are imaged onto the retina (Figure 1.2a). In this case the
input is the light signal incident at the cornea. One-dimensional patterns have a
constant intensity along the, say, horizontal dimension and varies along the
perpendicular (vertical) dimension. We will call the pattern of light intensity we
measure at the monitor screen the monitor image. We can measure the intensity of the
one-dimensional image by placing a light-sensitive device called a photodetector at
different positions on the screen. The vertical graph in Figure 1.2b shows a
measurement of the intensity of the monitor image at all screen locations.
The output of the optical transformation is the image formed at the retina. When the
input image is one-dimensional, the retinal image will be one-dimensional, too.
Hence, we can represent it using a curve as in Figure 1.2c. We will discuss the optical
components of the visual system in more detail later in this chapter, but from simply
looking at a picture of the eye in Figure 1.1 we can see that the monitor image passes
through a lot of biological material before arriving at the retina. Because the optics of
the eye are not perfect, the retinal image is not an exact copy of the monitor image:
The retinal image is a blurred copy of the input image.
Screen position
Retinal position
Figure 1.2: Retinal image formation illustrated with a single-line input image. (a) A
one-dimensional monitor image consists of a set of lines at different intensities. The
image is brought to focus on the retina by the cornea and lens. (b) We can represent
the intensity of a one-dimensional image using a simple graph that shows the light as
a function of horizontal screen position. Only a single value is plotted since the onedimensional image is constant along the vertical dimension. (c) The retinal image
is a blurred version of the one-dimensional input image. The retinal image is also
one-dimensional and is also represented by a single curve.
Examiner’s eye
Position 2
Position 1
Examiner’s eye
Subject’s eye
Subject’s eye
Figure 1.3: Principles of the ophthalmoscope. An ophthalmoscope is used to see an image
reflected from the interior of the eye. (a) When we look directly into the eye, we cast
a shadow making it impossible to see light reflected from the interior of the eye. (b)
The ophthalmoscope permits us to see light reflected from the interior of the eye.
Helmholtz invented the first ophthalmoscope. (After Cornsweet, 1970).
The image in Figure 1.2b shows one example of an infinite array of possible input
images. Since there is no hope of measuring the response to every possible input, to
characterize optical blurring completely we must build a model that specifies how
any input image is transformed into a retinal image. We will use linear systems
methods to develop a method of predicting the retinal image from any input image.
1.2 Reflections From the Eye
To study the optics of a human eye you will need an experimental eye, so you might
invite a friend to dinner. In addition, you will need a light source, such as a candle,
as a stimulus to present to your friend’s eye. If you look directly into your friend’s
eye, you will see a mysterious darkness that has beguiled poets and befuddled
visual scientists. The reason for the darkness can be understood by considering the
problem of ophthalmoscope design illustrated in Figure 1.3a.
If the light source is behind you, so that your head is between the light source and
the eye you are studying, then your head will cast a shadow that interferes with the
light from the point source arriving at your friend’s eye. As a result, when you look
in to measure the retinal image you see nothing beyond what is in your heart. If you
move to the side of the light path, the image at the back of your friend’s eye will be
reflected towards the light source, following a reversible path. Since you are now on
the side, out of the path of the light source, no light will be sent towards your eye1
The great nineteenth century scientist, H. von Helmholtz, built the first ophthalmoscope. The ophthalmoscope design shown in Figure 1.3 is unconventional, though it does include the basic principle:
Image of
light reflected
from the eye
Figure 1.4: A modified opthalmoscope measures the human retinal image. Light from a
bright source passes through a slit and into the eye. A fraction of the light is reflected
from the retina and is imaged. The intensity of the reflected light is measured at different spatial positions by varying the location of the analyzing slit. (After Campbell
and Gubisch, 1967).
Flamant (1955) first measured the retinal image using a modified ophthalmoscope.
She modified the instrument by placing a light sensitive recording, a photodetector, at
the position normally reserved for the ophthalmologist’s eye. In this way, she
measured the intensity pattern of the light reflected from the back of the observer’s
eye. Campbell and Gubisch (1967) used Flamant’s method to build their apparatus,
which is sketched in Figure 1.4. Campbell and Gubisch measured the reflection of a
single bright line, that served the input stimulus in their experiment. As shown in
the Figure, a beam-splitter placed between the input light and the observer’s eye
divides the input stimulus into two parts. The beam-splitter causes some of the light
to be turned away from the observer and lost; this stray light is absorbed by a light
baffle. The rest of the light continues toward the observer. When the light travels in
this direction, the beam-splitter is an annoyance, serving only to lose some of the
light; it will accomplish its function on the return trip.
The light that enters the observer’s eye is brought to a good focus on the retina by a
lens. A small fraction of the light incident on the retina is reflected and passes – a
second time – through the optics of the eye. On the return path of the light, the
beam-splitter now plays its functional role. The reflected image would normally
return to a focus at the light source. But the beam-splitter divides the returning
beam so that a portion of it is brought to focus in a measurement plane to one side of
the apparatus. Using a very fine slit in the measurement plane, with a photodetector
We need to arrange a light path so that the examiner’s eye does not cast a shadow. A bright source
light is required since the back of the human eye is not very reflective. An more conventional design
is in Appendix III of Visual Perception by T. Cornsweet.
Light path
Figure 1.5: The retina contains the light sensitive photoreceptors where light is focussed. This cross-section of a monkey retina outside the fovea shows there are several layers of neurons in the optical path between the lens and the photoreceptors. As
we will see later, in the central fovea these neurons are displaced to leaving a clear
optical path from the lens to the photoreceptors (Source: Boycott and Dowling, 1969).
behind it, Campbell and Gubisch measured the reflected light and used the
measurements of the reflected light to infer the shape of the image on the retinal
What part of the eye reflects the image? In Figure 1.5 we see a cross-section of the
peripheral retina. In normal vision, the image is focused on the retina at the level of
the photoreceptors. The light measured by Campbell and Gubisch probably contains
components from several different planes at the back of the eye. Thus, their
measurements probably underestimate the quality of the image at the level of the
Figure 1.6 shows several examples of Campbell and Gubisch’s measurements of the
light reflected from the eye when the observer is looking at a very fine line. The
different curves show measurements for different pupil sizes. When the pupil was
wide open (top, 6.6mm diameter) the reflected light is blurred more strongly than
when the pupil is closed (middle, 2.0mm). Notice that the measurements made with
a large pupil opening are less noisy; when the pupil is wide open more light passes
into the eye and more light is reflected, improving the quality of the measurements.
The light measured in Figure 1.6 passed through the optical elements of the eye
twice, while the retinal image passes through the optics only once. It follows that the
spread in these curves is wider than the spread we would observe had we measured
Pupil diameter
Intensity of
light reflected
from the eye
Visual angle (minutes of arc)
Figure 1.6: Experimental measurements of light that has been reflected from a human
eye looking at a fine line. The reflected light has been blurred by double passage
through the optics of the eye. (Source: Campbell and Gubisch, 1967).
at the retina. How can we use these doublepass measurements to estimate the blur at
the retina? To solve this problem, we must understand the general features of their
experiment. It is time for some theory.
1.3 Linear Systems Methods
A good theoretical account of a transformation, such as the mapping from monitor
image to retinal image, should have two important features. First, the theoretical
account should suggest to us which measurements we should make to characterize
the transformation fully. Second, the theoretical account should tell us how to use
these measurements to predict the retinal image distribution for all other monitor
In this section we will develop a set of general tools, referred to as linear systems
methods. These tools will permit us to solve the problem of estimating the optical
transformation from the monitor to the retinal image. The tools are sufficiently
general, however, that we will be able to use them repeatedly throughout this book.
There is no single theory that applies to all measurement situations. But, linear
systems theory does apply to many important experiments. Best of all, we have a
simple experimental test that permits us to decide whether linear systems theory is
appropriate to our measurements. To see whether linear systems theory is
Monitor image
Retinal image
Figure 1.7: The principle of homogeneity illustrated. An input stimulus and corresponding retinal image are shown in each part of the figure. The three input stimuli are
the same except for a scale factor. Homogeneity is satisfied when the corresponding
retinal images are scaled by the same factor. Part (a) shows an input image at unit
intensity, while (b) and (c) show the image scaled by 0.5 and 2.0 respectively.
appropriate, we must check to see that our data satisfy the two properties of
homogeneity and superposition.
A test of homogeneity is illustrated in Figure 1.7. The left-hand panels show a series of
monitor images, and the right-hand panels show the corresponding measurements
of reflected light2 . Suppose we represent the intensities of the lines in the
one-dimensional monitor image using the vector (upper left) and we represent the
retinal image measurements by the vector . Now, suppose we scale the input signal
by a factor , so that the new input is . We say that the system satisfies
homogeneity if the output signal is also scaled by the same factor of , and thus the
new output is . For example, if we halve the input intensity, then the reflected
We will use vectors and matrices in our calculations to eliminate burdensome notation Matrices
will be denoted by boldface, upper case Roman letters, . Column vectors will be denoted using
lower case boldface Roman letters, . The transpose operation will be denoted by a superscript T,
Scalar values will be in normal typeface, and they will usually be denoted using Roman characters ( )
except when tradition demands the use of Greek symbols (). The entry of a vector, , is a scalar
and will be denoted as . The column of a matrix, , is a vector that we denote as . The scalar
entry in the row and column of the matrix
will be denoted .
Monitor image
Retinal image
Figure 1.8: The principle of superposition illustrated. Each of the three parts of the picture shows an input stimulus and the corresponding retinal image. The stimulus in
part (a) is a single-line image and in part (b) the stimulus is a second line displaced
from the first. The stimulus in part (c) is the sum of the first two lines. Superposition
holds if the retinal image in part (c) is the sum of the retinal images in parts (a) and
light measured at their photodetector should be one-half the intensity (middle
panel). If we double the light intensity, the response should double (bottom panel).
Campbell and Gubisch’s measurements of light reflected from the human eye satisfy
Superposition, used as both an experimental procedure and a theoretical tool, is
probably the single most important idea in this book. You will see it again and again
in many forms. We describe it here for the first time.
Suppose we measure the response to two different input stimuli. For example,
suppose we find that input pattern (top left) generates the response (top right),
and input pattern (middle left) generates response (middle right). Now we
measure the response to a new input stimulus equal to the sum of and . If the
response to the new stimulus is the sum of the responses measured singly, ,
then the system is a linear system. By measuring the responses stimuli individually
and then the response to the sum of the stimuli, we test superposition. When the
responses to sum of the stimuli equals the sum of the individual responses, then we
say the system satisfies superposition. Campbell and Gubisch’s measurements of
light reflected from the eye satisfy this principle.
We can summarize homogeneity and superposition succinctly using two equations.
Write the linear optical transformation that maps the input image to the light
intensity at each of the receptors as
Homogeneity and superposition are defined by the pair of equations 3
Implications of Homogeneity and Superposition
Figure 1.9 illustrates how we will use linear systems methods to characterize the
relationship between the input signal from a monitor, light reflected from the eye4 .
First, we make an initial set of measurements of the light reflected from the eye for
each single-line monitor image, with the line set to unit intensity. If we know the
images from single-line images, and we know the system is linear, then we can
calculate the light reflected from the eye from any monitor image: Any
one-dimensional image is the sum of a collection of lines.
Consider an arbitrary one-dimensional image, as illustrated at the top of Figure 1.9.
We can conceive of this image as the sum of a set of single-line monitor images, each
at its own intensity, . We have measured the reflected light from each single-line
image alone, call this for the line. By homogeneity it follows that the reflected
light from line will be a scaled version of this response, namely .. Next, we
combine the light reflected from the single-line images. By superposition, we know
that the light reflected from the original monitor image, , is the sum of the light
reflected from the single-line images,
Notice that the superposition leads us to expect homogeneity for integer scalars since
and in general if we sum copies of Ô
We write the homogeneity separately from superposition to avoid the tedium of treating the case of
irrational numbers in certain proofs.
We analyze a one-dimensional monitor images to simplify the notation. The principles remain the
same, but the notation becomes cumbersome, when we consider two-dimensional images.
p r 2 + p r3
= p1 r 1
Figure 1.9: Application of homogeneity and superposition. (a) A one-dimensional monitor
image is the weighted sum of a set of lines. An example of a one-dimensional image
is shown on the left and the individual monitor lines comprising the monitor image
are shown separately on the right. (b) Each line in the component monitor image
contributes to the retinal image. The retinal images created by the individual lines
are shown below the individual monitors. The sum of the retinal images is shown on
the left. (c) The retinal image generated by the monitor line at unit intensity is represented by the vector . The intensity of the monitor line is . By homogeneity,
the retinal image of the monitor line is . By superposition, the retinal image of
the collection of monitor lines is sum of the individual retinal images, .
r =
r1 r2
r = Rp
Figure 1.10: Matrix multiplication is a convenient notation for linear systems methods.
For example, the weighted sum of a set of vectors, as in part (c) of Figure 1.9, can be
represented using matrix multiplication. The matrix product equals the sum of the
columns of weighted by the entries of . When the matrix describes the responses
of a linear system, we call it a system matrix.
Equation 1.4 defines a transformation that maps the input stimulus, , into the
measurement, . Because of the properties of homogeneity and superposition, the
transformation is the weighted sum of a fixed collection of vectors: When the
monitor image varies, only the weights in the formula, , vary but the vectors , the
reflections from single-line stimuli, remain the same. Hence, the reflected light will
always be the weighted sum of these reflections.
To represent the weighted sum of a set of vectors, we use the mathematical notation
of matrix multiplication. As shown in Figure 1.10, multiplying a matrix times a vector
computes the weighted sum of the matrix columns; the entries of the vector define
the weights. Matrix multiplication and linear systems methods are closely linked. In
fact, the set of all possible matrices define the set of all possible linear
transformations of the input vectors.
Matrix multiplication has a shorthand notation to replace the explicit sum of vectors
in Equation 1.4. In the example here, we define a matrix, , whose columns are the
responses to individual monitor lines at unit intensity, . The matrix is called the
system matrix. Matrix multiplication of the input vector, , times the system matrix
, transforms the input vector into the output vector. Matrix multiplication is
written using the notation
Matrix multiplication follows naturally from the properties of homogeneity and
superposition. Hence, if a system satisfies homogeneity and superposition, we can
describe the system response by creating a system matrix that transforms the input to
the output.
A numerical example of a system matrix. Let’s use a specific numerical example
to illustrate the principle of matrix multiplication. Suppose we measure a monitor
that displays only three lines. We can describe the monitor image using a column
vector with three entries, .
The lines of unit intensity are , and . We measure the
response to these input vectors to build the system matrix. Suppose the
measurements for these three lines are , ,
and respectively. We place these responses into the columns of
the system matrix:
We can predict the response to any monitor image using the system matrix. For
example, if the monitor image is we multiply the input vector and
the system matrix to obtain the response, on the left side of Equation 1.7.
Why linear methods are useful
Linear systems methods are a good starting point for answering an essential
scientific question: How can we generalize from the results of measurements using a
few stimuli to predict the results we will obtain when we measure using novel
stimuli? Linear systems methods tell us to examine homogeneity and superposition.
If these empirical properties hold in our experiment, then we will be able to measure
responses to a few stimuli and predict responses to many other stimuli.
This is very important advice. Quantitative scientific theories are attempts to
characterize and then explain systems with many possible input stimuli. Linear
systems methods tell us how to organize experiments to characterize our system:
measure the responses to a few individual stimuli, and then measure the responses
to mixtures of these stimuli. If superposition holds, then we can obtain a good
characterization of the system we are studying. If superposition fails, your work will
not be wasted since you will need to explain the results of superposition
experiments to obtain a complete characterization of the measurements.
To explain a system, we need to understand the general organizational principles
concerning the system parts and how the system works in relationship to other
systems. Achieving such an explanation is a creative act that goes beyond simple
characterization of the input and output relationships. But, any explanation must
begin with a good characterization of the processing the system performs.
1.4 Shift-Invariant Linear Transformations
Shift-Invariant Systems: Definition
Since homogeneity and superposition are well satisfied by Campbell and Gubisch’s
experimental data, we can predict the result of any input stimulus by measuring the
system matrix that describes the mapping from the input signal to the
measurements at the photodetector. But the experimental data are measurements of
light that has passed through the optical elements of the eye twice, and we want to
know the transformation when we pass through the optics once. To correct for the
effects of double passage, we will take advantage of a special property of optics of
the eye, shift-invariance. Shift-invariant linear systems are an important class of linear
systems, and they have several properties that make them simpler than general
linear systems. The following section briefly describes these properties and how we
take advantage of them. The mathematics underlying these properties is not hard; I
sketch proofs of these properties in the Appendix.
Suppose we start to measure the system matrix for the Campbell and Gubisch
experiment by measuring responses to different lines near the center of the monitor.
Because the quality of the optics of our eye is fairly uniform near the fovea, we will
find that our measurements, and by implication the retinal images, are nearly the
same for all single-line monitor images. The only way they will differ is that as the
position of the input translates, the position of the output will translate by a
corresponding amount. The shape of the output, however, will not change. An
example of two measurements we might find when we measure using two lines on
the monitor is illustrated in the top two rows of Figure 1.8. As we shift the input
line, the measured output shifts. This shift is a good feature for a lens to have,
because as an object’s position changes, the recorded image should remain the same
(except for a shift). When we shift the input and the form of the output is invariant,
we call the system shift-invariant.
Shift-Invariant Systems: Properties
We can define the system matrix of a shift-invariant system from the response to a
single stimulus. Ordinarily, we need to build the system matrix by combining the
responses to many individual lines. The system matrix of a linear shift-invariant
system is simple to estimate since these responses are all the same except for a shift.
Hence, if we measure a single column of the matrix, we can fill in the rest of the
matrix. For a shift-invariant system, there is only one response to a line. This
response is called the linespread of the system. We can use the linespread function to
fill in the entire system matrix.
The response to a harmonic function at frequency is a harmonic function at the
same frequency. Sinusoids and cosinusoids are called harmonics or harmonic
functions. When the input to shift-invariant system is a harmonic at frequency , the
output will be a harmonic at the same frequency. The output may be scaled in
amplitude and shifted in position, but it still will be a harmonic at the input
For example, when the input stimulus is defined at points and at these points its
values are sinusoidal, . Then, the response of a shift-invariant system will be
a scaled and shifted sinusoid, . There is some uncertainty concerning
the output because there two unknown values, the scale factor, , and phase shift,
. But, for each sinusoidal input we know a lot about the output; the output will be
a sinusoid of the same frequency as the input.
We can express this same result another useful way. Expanding the sinusoidal
output using the summation rule we have
In other words, when the input is a sinusoid at frequency , the output is the
weighted sum of a sinusoid and a cosinusoid, both at the same frequency as the
input. In this representation, the two unknown values are the weights of the
sinusoid and the cosinusoid.
For many optical systems, such as the human eye, the relationship between
harmonic inputs and the output is even simpler. When the input is a harmonic
Spatial position
Figure 1.11: Double passage. (a) The amplitude, , of an input cosinusoid stimulus is
scaled by a factor, , after passing through even-symmetric shift-invariant symmetric
optics as shown in part (b). (c) Passage through the optics a second time scales the
amplitude again, resulting in a signal with amplitude .
function at frequency , the output is a scaled copy of the function and there is no
the output will be
shift in spatial phase. For example, when the input is , and only the scale factor, which depends on frequency, is unknown.
1.5 The Optical Quality of the Eye
We are now ready to correct the measurements for the effects of double passage
through the optics of the eye. To make the method easy to understand, we will
analyze how to do the correction by first making the assumption that the optics
introduce no phase shift into the retinal image; this means, for example, that a
cosinusoidal stimulus creates a cosinusoidal retinal image, scaled in amplitude. It is
not necessary to assume that there is no phase shift but the assumption is reasonable
and the main principles of the analysis are easier to see if we assume there is no
phase shift.
To understand how to correct for double passage, consider a hypothetical alternative
experiment Campbell and Gubisch might have done (Figure 1.11). Suppose
Campbell and Gubisch had used input stimuli equal to cosinusoids at various
spatial frequencies, . Because the optics are shift-invariant and there is no
frequency-dependent phase shift, the retinal image of a cosinusoid at frequency is
a cosinusoid scaled by a factor . The retinal image passes back through the optics
and is scaled again, so that the measurement would be a cosinusoid scaled by the
factor . Hence, had Campbell and Gubisch used a cosinusoidal input stimulus,
we could deduce the retinal image from the measured image easily: The retinal
image would be a cosinusoid with an amplitude equal to the square root of the
amplitude of the measurement.
Campbell and Gubisch used a single line, not a set of cosinusoidal stimuli. But, we
can still apply the basic idea of the hypothetical experiment to their measurements.
Their input stimulus, defined over locations, is
if if (1.10)
As I describe in the appendix, we can express the stimulus as the weighted sum of
harmonic functions by using the discrete Fourier series. The representation of a single
line is equal to the sum of cosinusoidal functions
Because the system is shift-invariant, the retinal image of each cosinusoid was a
scaled cosinusoid, say with scale factor . The retinal image was scaled again
during the second pass through the optics, to form the cosinusoidal term they
measured. 5
Using the discrete Fourier series, we also can express the measurement as the sum of
cosinusoidal functions,
Measurement (1.12)
We know the values of , since this was Campbell and Gubisch’s measurement.
The image of the line at the retina, then, must have been
The values define the linespread function of the eye’s optics. We can correct for the
double passage and estimate the linespread because the system is linear and
Be bothered by the fact that the discrete Fourier series approximation is an infinite set of pulses,
rather than a single line. To understand why, consult the Appendix.
Visual angle (minutes of arc)
Figure 1.12: The linespread function of the human eye. The solid line in each panel is a
measurement of the linespread. The dotted lines are the diffraction-limited linespread
for a pupil of that diameter. (Diffraction is explained later in the text). The different
panels show measurements for a variety of pupil diameters (From Campbell and
Gubisch, 1967).
As you read further about experimental and computational methods in vision
science, remember that there is nothing inherently important about sinusoids as
visual stimuli; we must not confuse the stimulus with the system or with the theory
we use to analyze the system. When the system is a shift-invariant linear system,
sinusoids can be helpful in simplifying our calculations and reasoning, as we have
just seen. The sinusoidal stimuli are important only insofar as they help us to
measure or clarify the properties of the system. And if the system is not
shift-invariant, the sinusoids may not be important at all.
The Linespread Function
Figure 1.12 contains Campbell and Gubisch’s estimates of the linespread functions
of the eye. Notice that as the pupil size increases, the width of the linespread
function increases which indicates that the focus is worse for larger pupil sizes. As
the pupil size increases, light reaches the retina through larger and larger sections of
the lens. As the area of the lens affecting the passage of light increases, the amount
of blurring increases.
The measured linespread functions, , along with our belief that we are studying a
shift-invariant linear system, permit us to predict the retinal image for any
Relative intensity
Visual angle (minutes of arc)
Figure 1.13: An analytic approximation of the human linespread function for an eye with a
3.0mm diameter pupil (Westheimer, 1986).
one-dimensional input image. To calculate these predictions, it is convenient to have
a function that describes the linespread of the human eye. G. Westheimer (1986)
suggested the following formula to describe the measured linespread function of the
human eye, when in good focus, and when the pupil diameter is near 3mm.
where the variable refers to position on the retina specified in terms of minutes of
visual angle. A graph of this linespread function is shown in Figure 1.13
We can use Westheimer’s linespread function to predict the retinal image of any
one-dimensional input stimulus6 . Some examples of the predicted retinal image are
shown in Figure 1.14. Because the optics blurs the image, even the light from a very
fine line is spread across several photoreceptors. We will discuss the relationship
between the optical defocus and the positions of the photoreceptors in Chapter ??.
The Modulation Transfer Function
In correcting for double passage, we thought about the measurements in two
separate ways. Since our main objective was to derive the linespread function, a
function of spatial position, we spent most of our time thinking of the measurements
Westheimer’s linespread function is for an average observer under one set of viewing conditions.
As the pupil changes size and as observer’s age, the linespread function can vary. Consult IJspeert et
al. (1993) and Williams et al. (1995) for alternatives to Westheimer’s formula.
Visual angle (minutes of arc)
Photoreceptor positions
Figure 1.14: Examples of the effect of optical blurring. (a) Images of a line, edge and a bar
pattern. (b) The estimated retinal image of the images after blurring by Westheimer’s
linespread function. The spacing of the photoreceptors in the retina is shown by the
stylized arrows.
Amplitude scale factor
Williams et al.
Spatial frequency (cycles per degree)
Figure 1.15: Modulation transfer function measurements of the optical quality of the
lens made using visual interferometry (Williams et al., 1995; described in Chapter
??). The data are compared with the predictions from the linespread suggested by
Westheimer (1984) and a curve fit through the data by Williams et al. (1995).
in terms of light intensity as a function of spatial position. When we corrected for
double passage through the optics, however, we also considered a hypothetical
experiment in which the stimuli were harmonic functions (cosinusoids). To perform
this calculation, we found that it was easier to correct for double passage by thinking
of the stimuli as the sum of harmonic functions, rather than as a function of spatial
These two ways of looking at the system, in terms of spatial functions or sums of
harmonic functions, are equivalent to one another. To see this, notice that we can use
the linespread function to derive the retinal image to any input image. Hence, we
can use the linespread to compute the scale factors of the harmonic functions.
Conversely, we already saw that by measuring how the system responds to the
harmonic functions, we can derive the linespread function. It is convenient to be
able to reason about system performance in both ways.
The optical transfer function defines the system’s complete response to harmonic
functions. The optical transfer function is a complex-valued function of spatial
frequency. The complex values code both the scale factor and the phase shift the
system induces in each harmonic function.
When the linespread function of the eye is an even-symmetric function, there is no
phase shift of the harmonic functions. In this case, we can describe the system
completely using a real valued function, the modulation transfer function. This
function defines the scale factors applied to each spatial frequency. The data points
in Figure 1.15 show measurements of the modulation transfer function of the human
eye. These data points were measured using a method called visual interferometry
that is described in Chapter ??. Along with the data points in Figure 1.15, I have
plotted the predicted modulation transfer function using Westheimer’s linespread
function and a curve fit to the data by Williams et al. (1995). The curve derived by
Westheimer (1986) using completely different data sets differs from the
measurements by Williams et al. (1995) by no more than about twenty percent. This
should tell you something about the relative precision of these descriptions of the
optical quality of the lens.
The linespread function and the modulation transfer function offer us two different
ways to think about the optical quality of the lines. The linespread function in Figure
1.13, describes defocus as the spread of light from a fine slit across the
photoreceptors: the light is spread across three to five photoreceptors. The
modulation transfer function in Figure 1.15 describes defocus as an amplitude
reduction of harmonic stimuli: beyond 12 cycles per degree the amplitude is
reduced by more than a factor of two.
Figure 1.16: Snell’s law. The solid lines indicate surface normals and the dashed lines
indicate the light ray. (a) When a light ray passes from one medium to another, the
ray can be refracted so that the angle of incidence () does not equal the angle of
refraction ( ). Instead, the angle of refraction depends on the refractive indices of the
new media ( and ) a relationship called Snell’s law that is defined in Equation 1.15.
(b) A prism causes two refractions of the light ray and can reverse the ray’s direction
from upward to downward. (c) A lens combines the effect of many prisms in order
to converge the rays diverging from a point source.
1.6 Lenses, Diffraction and Aberrations
Lenses and Accommodation
What prevents the optics of our eye from focusing the image perfectly? To answer
this question we should consider why a lens is useful in bringing objects to focus at
As a ray of light is reflected from an object, it will travel along along a straight line
until it reaches a new material boundary. At that point, the ray may be either
absorbed by the new medium, reflected, or refracted. The latter two possibilities are
illustrated in part (a) of Figure 1.16. We call the angle between the incident ray of
light and the perpendicular to the surface the angle of incidence. The angle between
the reflected ray and the perpendicular to the surface is called the angle of reflection,
and it equals the angle of incidence. Of course, reflected light is not useful for image
formation at all.
The useful rays for imaging must pass from the first medium into the second. As
they pass from between the two media, the ray’s direction is refracted. The angle
between the refracted ray and the perpendicular to the surface is called the angle of
The relationship between the angle of incidence and the angle of refraction was first
discovered by a Dutch astronomer and mathematician, Willebrord Snell in 1621. He
observed that when is the angle of incidence, and is the angle of refraction, then
The terms and in Equation 1.15 are the refractive indices of the two media. The
refractive index of an optical medium is the ratio of the speed of light in a vacuum to
the speed of light in the optical medium. The refractive index of glass is , for
water the refractive index is and for air it is nearly . The refractive index
of the human cornea is is quite similar to water, which is the main content of
our eyes.
Now, consider the consequence of applying Snell’s law twice in a row as light passes
into and then out of a prism, as illustrated in part (b) of Figure 1.16. We can draw the
path of the ray as it enters the prism using Snell’s law. The symmetry of the prism
and the reversibility of the light path makes it easy to draw the exit path. Passage
through the prism bends the ray’s path downward. The prism causes the light to
deviate significantly from a straight path; the amount of the deviation depends upon
the angle of incidence and the angle between the two sides of the prism.
We can build a lens by smoothly combining many infintesimally small prisms to
form a convex lens, as illustrated in part (c) of Figure 1.16. In constructing such a
lens, any deviations from the smooth shape, or imperfections in the material used to
build the lens, will cause the individual rays to be brought to focus at slightly
different points in the image plane. These small deviations of shape or materials are
a source of the imperfections in the image.
Objects at different depths are focused at different distances behind the lens. The
lensmaker’s equation relates the distance between the source and the lens with the
distance between the image and the lens. The lensmaker’s equation relating these
two distances depends on focal length of the lens. Call the distance from the center of
the lens to the source , the distance to the image , and the focal length of the lens,
. Then the lensmaker’s equation is
From this equation, notice that we can measure the focal length of a convex thin lens
by using it to image a very distant object. In that case, the term is zero so that
the image distance is equal to the focal length. When I first moved to California, I
spent a lot of time measuring the focal length of the lenses in my laboratory by going
Image distance (m)
Distance from
lens to
Source distance (m)
Figure 1.17: Depth of field of the human eye. Image distance is shown as a function of
source distance. The bar on the vertical axis shows the distance of the retina from
the lens center. A lens power of 60 diopters brings distant objects into focus, but not
nearby objects; to bring nearby objects into focus the power of the lens must increase.
The depth of field, namely the distance over which objects will continue to be in
reasonable focus, can be estimated from the slope of the curve.
outside and imaging the sun on a piece of paper behind the lens; the sun was a
convenient source at optical infinity. It had been a less reliable source for me in my
previous home.
The optical power of a lens is a measure of how strongly the lens bends the incoming
rays. Since a short focal length lens bends the incident ray more than a long focal
length lens, the optical power is the inversely related to focal length. The optical
power is defined as the reciprocal of the focal length measured in meters and is
specified in units of diopters. When we view far away objects, the distance from the
middle of the cornea and the flexible lens to the retina is 0.017m. Hence, the optical
power of the human eye is , or roughly 60 diopters.
From the optical power of the eye ( ) and the lensmaker’s equation, we can
calculate the image distance of a source at distance. For example, the top curve in
Figure 1.17 shows the relationship between image distance and source distance
for a 60 diopter lens. Sources beyond 1.0m are imaged at essentially the same
distance behind the optics. Sources closer than 1.0m are imaged at a longer distance,
so that the retinal image is blurred.
To bring nearby sources into focus on the retina, muscles attached to the lens change
its shape and thus change the power of the lens. The bottom two curves in Figure
Figure 1.18: Pinhole optics. Using ray-tracing, we see that only a small pencil of rays
passes through a pinhole. (a) If we widen the pinhole, light from the source spread
across the image, making it blurry. (b) If we narrow the pinhole, only a small amount
of light is let in. The image is sharp; the sharpness is limited by diffraction.
1.17 illustrate that sources closer than 1.0m can be focused onto the retina by
increasing the power of the lens. The process of adjusting the focal length of the lens
is called accommodation. You can see the effect of accommodation by first focusing on
your finger placed near your noise and noticing that objects in the distance appear
blurred. Then, while leaving your finger in place, focus on the distant objects. You
will notice that your finger now appears blurred.
Pinhole Optics and Diffraction
The only way to remove lens imperfections completely is to remove the lens. It is
possible to focus images without any lens at all by using pinhole optics, as illustrated
in Figure 1.18. A pinhole serves as a useful focusing element because only the rays
passing within a narrow angle are used to form the image. As the pinhole is made
smaller, the angular deviation is reduced. Reducing the size of the pinhole serves to
reduce the amount of blur due to the deviation amongst the rays. Another
advantage of using pinhole optics is that no matter how distant the source point is
from the pinhole, the source is rendered in sharp focus. Since the focusing is due to
selecting out a thin pencil of rays, the distance of the point from the pinhole is
irrelevant and accommodation is unnecessary.
But the pinhole design has two disadvantages. First, as the pinhole aperture is
Figure 1.19: Diffraction limits the quality of pinhole optics. The three images of a bulb
filament were imaged using pinholes with decreasing size. (a) When the pinhole is
relatively large, the image rays are not properly converged and the image is blurred.
(b) Reducing the pinhole improves the focus. (c) Reducing the pinhole further worsens the focus due to diffraction.
reduced, less and less of the light emitted from the source is used to form the image.
The reduction of signal has many disadvantages for sensitivity and acuity.
A second fundamental limit to the pinhole design is a physical phenomenon. When
light passes through a small aperture, or near the edge of an aperture, the rays do
not travel in a single straight line. Instead, the light from a single ray is scattered into
many directions and produces a blurry image. The dispersion of light rays that pass
by an edge or narrow aperture is called diffraction. Diffraction scatters the rays
coming from a small source across the retinal image and therefore serves to defocus
the image. The effect of diffraction when we take an image using pinhole optics is
shown in Figure 1.19.
Diffraction can be explained in two different ways. First, diffraction can be
explained by thinking of light as a wave phenomenon. A wave exiting from a small
aperture expands in all directions; a pair of coherent waves from adjacent apertures
create an interference pattern. Second diffraction can be understood in terms of
quantum mechanics; indeed, the explanation of diffraction is one of the important
achievements of quantum mechanics. Quantum mechanics supposes that there are
limits to how well we may know both the position and direction of travel of a photon
of light. The more we know about a photon’s position, the less we can know about
its direction. If we know that a photon has passed through a small aperture, then we
know something about the photon’s position and we must pay a price in terms of
our uncertainty concerning its direction of travel. As the aperture becomes smaller,
our certainty concerning the position of the photon becomes greater; this uncertainty
Figure 1.20: Diffraction pattern caused by a circular aperture. (a) The image of a diffraction pattern measured through a circular aperture. (b) A graph of the cross-sectional
intensity of the diffraction pattern. (After Goodman, 1968).
takes the form of the scattering of the direction of travel of the photons as they pass
through the aperture. For very small apertures, for which our position certainty is
high, the photon’s direction of travel is very broad producing a very blurry image.
There is a close relationship between the uncertainty in the direction of travel and
the shape of the aperture (see Figure 1.20). In all cases, however, when the aperture
is relatively large, our knowledge of the spatial position of the photons is
insignificant and diffraction does not contribute to defocus. As the pupil size
decreases, and we know more about the position of the photons, the diffraction
pattern becomes broader and spoils the focus.
In the human eye diffraction occurs because light must pass through the circular
aperture defined by the pupil. When the ambient light intensity is high, the pupil
may become as small mm in diameter. For a pupil opening this small, the optical
blurring in the human eye is due only to the small region of the cornea and lens near
the center of our visual field. With this small an opening of the pupil, the quality of
the cornea and lens is rather good and the main source of image blue is diffraction.
At low light intensities, the pupil diameter is as large as 8 mm. When the pupil is
open quite wide, the distortion due to cornea and lens imperfections is large
compared to the defocus due to diffraction.
One way to evaluate the quality of the optics is to compare the blurring of the eye to
the blurring from diffraction alone. The dashed lines in Figure 1.12 plot the blurring
expected from diffraction for different pupil widths. Notice that when the pupil is
2.4 mm, the observed linespread is about equal to the amount expected by
diffraction alone; the lens causes no further distortion. As the pupil opens, the
observed linespread is worse than the blurring expected by diffraction alone. For
these pupil sizes the defocus is due mainly to imperfections in the optics7 .
Helmholtz calculated that this was so long before any precise measurements of the optical quality
Figure 1.21: A pointspread function (a) and the sum of two pointspreads (b). The
pointspread function is the image created by a source consisting of a small point of
light. When the optics shift-invariant, the image to any stimulus can be predicted
from the pointspread function.
The Pointspread Function and Astigmatism
Most images, of course, are not composed of weighted sums of lines. The set of
images that can be formed from sums of lines oriented in the same direction are all
one-dimensional patterns. To create more complex images, we must either use lines
with different orientations or use a different fundamental stimulus: the point.
Any two-dimensional image can be described as the sum of a set of points. If the
system we are studying is linear and shift-invariant, we can use the response to a
point and the principle of superposition to predict the response of a system to any
two-dimensional image. The measured response to a point input is called the
pointspread function. A pointspread function and the superposition of two nearby
pointspreads are illustrated in Figure 1.21.
Since lines can be formed by adding together many different points, we can compute
the system’s linespread function from the pointspread. In general, we cannot deduce
the pointspread function from the linespread because there is no way to add a set of
lines, all oriented in the same direction, to form a point. If it is know that a
pointspread function is circularly symmetric, however, a unique pointspread
function can be deduced from the linespread function. The calculation is described
of the eye were possible. He wrote,
The limit of the visual capacity of the eye as imposed by diffraction, as far as it can be
calculated, is attained by the visual acuity of the normal eye with a pupil of the size
corresponding to a good illumination. (Helmholtz, 1909, p. 442)
Figure 1.22: Astigmatism implies an asymmetric pointspread function.
pointspread shown here is narrow in one direction and wide in another. The spatial resolution of an astigmatic system is better in the narrow direction than the wide
in the beginning of Goodman (1968) and in Yellott, Wandell and Cornsweet (1981).
When the pointspread functions is not circularly symmetric, measurements of the
linespread function will vary with the orientation of the test line. It may be possible
to adjust the accommodation of this type of system so that any single orientation is
in good focus, but it will be impossible to bring all orientations into good focus at
the same time. For the human eye, astigmatism can usually be modeled by
describing the defocus as being derived from the contributions of two
one-dimensional systems at right angles to one another. The defocus in intermediate
angles can be predicted from the defocus of these two systems.
Chromatic Aberration
The light incident at the eye is usually a mixture of different wavelengths. When we
measure the system response, there is no guarantee that the linespread or
pointspread function we measure with different wavelengths will be the same.
Indeed, for most biological eyes the pointspread function is very different as we
measure using different wavelengths of light. When the pointspread function of
different wavelengths of light is quite different, then the lens is said to exhibit
chromatic aberration.
When the incident light is the mixture of many different wavelengths, say white
Power of lens (diopters)
Wald and Griffin (1947)
Bedford and Wyszecki (1957)
Thibos et al. (1992)
Wavelength (nm)
Source at
Focal length
Lens power (diopters) =
Focal length (m)
Figure 1.23: Chromatic aberration of the human eye. (a) The data points are from Wald
and Griffin (1947), and Bedford and Wyszecki (1957). The smooth curve plots the
formula used by Thibos et al. (1992), where is wavelength in
micrometers, is the defocus in diopters, , , and .
This formula implies an in-focus wavelength of 578 nm. (b) The power of a thin lens is
the reciprocal of its focal length, which is the image distance from a source at infinity.
(After Marimont and Wandell, 1993).
cy (c
Figure 1.24: Two views of the modulation transfer function of a model eye at various wavelengths. The model eye has the same chromatic aberration as the human eye (see
Figure 1.23) and a 3.0mm pupil diameter. The eye is in focus at 580nm; the curve at
580nm is diffraction limited. The retinal image has no contrast beyond four cycles per
degree at short wavelengths. (From Marimont and Wandell, 1993).
light, then we can see a chromatic fringe at edges. The fringe occurs because the
different wavelength components of the white light are focused more or less sharply.
Figure 1.23a plots one measure of the chromatic aberration. The smooth curve plots
the lens power, measured in units of diopters needed to bring each wavelength into
focus along with a 578nm light.
Figure 1.23 shows the optical power of a lens necessary to correct for the chromatic
aberration of the eye. When the various wavelengths pass through the correcting
lens, the optics will have the same power as the eye’s optics at 578nm. The two sets
of measurements agree well with one another and are similar to what would be
expected if the eye were simply a bowl of water. The smooth curve through the data
is a curve used by Thibos et al. (1992) to predict the data.
An alternative method of representing the axial chromatic aberration of the eye is to
plot the modulation transfer function at different wavelengths. The two surface
plots in Figure 1.24 shows the modulation transfer function at a series of
wavelengths. The plots show the same data, but seen from different points of view
so that you can see around the hill. The calculation in the figure is based on an eye
with a pupil diameter of 3.0mm, the same chromatic aberration as the human eye,
and in perfect focus except for diffraction at 580nm.
The retinal image contains very poor spatial information at wavelengths that are far
from the best plane of focus. By accommodation, the human eye can place any
wavelength into good focus, but it is impossible to focus all wavelengths
simultaneously8 .
A possible method of improving the spatial resolution of the eye to different wavelengths of light
is to place the different classes of photoreceptors in slightly different image planes. Ahnelt et al. (1987)
and Curcio et al. (1991) have observed that the short-wavelength photoreceptors have a slightly different shape and length from the middle- and long-wavelength photoreceptors. In principle, this difference could play a role to compensate for the chromatic aberration of the eye. But, the difference is very
small, and it is unlikely that it plays any significant role in correcting for axial chromatic aberration.
1. Matrix calculations.
(a) Consider the matrix
Write the transpose of , the matrix Ì .
(b) Compute the result of multiplying with the column vector
(that is, compute ).
(c) Compute the result of multiplying with the column vector
(d) Compute the result of multiplying with the column vector
(e) Compute the result of multiplying by the matrix
(f) How do the results of multiplying by the matrix compare with the results
of multiplying by the individual vectors?
2. Answer the following questions about lens specifications.
(a) When your eye doctor prescribes lenses for you, she tells you the visual
correction you require in terms of diopters. What does it mean to require a
6-diopter optical correction?
(b) What does visual astigmatism mean?
(c) Do you think it is likely that different people vary greatly in the extent of
chromatic aberration in their eyes? Why or why not?
(d) Suppose one individual needs a 6-diopter correction and another
individual needs a 3-diopter correction. Give your best guess about the
relative linespread functions of the two individuals. Give your best guess
about the relative modulation transfer functions of the two individuals.
3. Answer these questions with respect to experimental estimates of the
linespread function of the optics.
(a) What instrumentation might Campbell and Gubisch have used to
measure the pointspread function of the eye? What additional problems
would they have had if they had measured the pointspread. (See papers
by Artal et al. (1989) for such measurements.)
(b) Can the linespread function be determined from the pointspread
function? If so, how?
(c) Can the pointspread function be determined from a single linespread
function? If so, how? (See the appendix in Yellott, Wandell, and
Cornsweet, 1980.)
(d) Do you think it is possible that Campbell and Gubisch measured the light
reflected precisely from the photoreceptor plane? Why or why not? If not,
how should we evaluate the linespread function that we estimate
compared to the linespread function in the plane of the photoreceptors?
(e) IJspeert et al. (1993) recently described a new set of equations to
characterize the optical linespread of the eye. These equations are
intended to generalize Westheimer’s function described in the text. Read
their paper and compare their new curves with Westheimer’s formula.
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