 CAMERA RESPONSE FUNCTION RECOVERY FROM
DIFFERENT ILLUMINATIONS OF IDENTICAL SUBJECT MATTER
Corey Manders, Chris Aimone, Steve Mann
University of Toronto
Dept. of Electrical and Computer Engineering
10 King's College Rd.
ABSTRACT
A robust method of camera response function estimation, using
pictures taken by differently illuminating the same subject mat-
ter, is presented. The method solves for the response function di-
rectly using superposition constraints imposed by different com-
binations of two (or more) lights to illuminate the same subject
matter. Previous methods of computing camera response functions
typically used differing exposures of identical subject matter, lead-
ing to uniqueness problems (underconstrained due to comparamet-
ric periodicity or fractal ambiguity). The method used in this paper
overcomes this problem. Finally, we compare the method of the
paper to previous methods and find the new method outperforms
the previous work.
1. INTRODUCTION: A SIMPLE CAMERA MODEL
While the geometric calibration of cameras is widely practiced and
understood , often much less attention is given to the camera
response function (how the camera responds to light). In digital
cameras, the camera response function maps the actual quantity of
light impinging on each element of the sensor array to the pixel
values that the camera outputs.
Linearity (which is typically not exhibited by most camera re-
sponse functions) implies the following two conditions:
1. Homogeneity: A function is said to exhibit homogeneity if
and only if f
(ax) = af (x), for all scalar a.
2. Superposition: A function is said to exhibit superposition if
and only if f
(x + y) = f (x) + f (y).
The two are often written together, as: f
(ax + by) = af (x) +
bf
(y). However, for the purposes of this paper we wish to consider
homogeneity and superposition separately.
In image processing, homogeneity arises when we compare
differently exposed pictures of the same subject matter. Super-
position arises when we superimpose (superpose) pictures taken
from differently illuminated instances of the same subject matter,
using a simple law of composition such as addition (i.e. using the
A variety of techniques have been proposed to recover camera
response functions, such as using charts of known reflectance, and
using different exposures of the same subject matter .
The method proposed in this paper differs from other methods in
that it does not require the use of charts, nor a camera that is ca-
pable of adjusting its exposure. The method is very easy to use,
produces very accurate results and requires only that the camera
has an exposure lock feature. Furthermore, the technique is useful
in situations where differently illuminated, rather than differently
exposed pictures are available. For example, a camera in a typical
building or dwelling may observe an at least partially static scene
during which time various lights are turned on and off throughout
the day.
The comparagram, as defined in  has been widely
used as a tool for the comparison of multiple differently exposed
pictures of the same subject matter. With enough data, a direct
nonparametric solution for the camera response function can be
obtained, otherwise, a semi-parametric method such as Candocia's
piecewise linear comparametric method will often provide better
results. A drawback of completely nonparametric methods is
that comparametric periodicity (periodicity in the amplitude do-
main, i.e. amplitude "ripples", also known as fractal ambiguity
and comperiodicity), plague the result unless more than two
input images are used with exposure differences that are inhar-
monic (in the amplitude domain).
The method we propose in this paper uses the notion of super-
position rather than homogeneity to solve for the camera response
function. In this method the linear constraint of superposition dis-
ambiguates comparametric periodicity.
The following technique is used: in a dark environment, set
up two distinct light sources. Take three pictures, one with each
light on individually (p
a
, p
b
), and one with the two lights on to-
gether (p
c
). From this data we solve for the camera response func-
tion f by using the following constraints: For the i
th
pixel posi-
tion in each of the three images: p
a
[i] = f (q
a
), p
b
[i] = f (q
b
),
and p
c
= f (q
a
+ q
b
). Where the quantity q is known as the
photographic quantity or photoquanity. Note that the pho-
nance, rather, it is a unit of light, unique to the spectral response
of a particular camera. The results obtained through this method
were more accurate than using homogeneity (e.g. comparagrams)
or typically available (coarsely quantized) charts.
In this paper we also propose an alternative construct which
we refer to as the superposigram. Just as the comparagram pro-
vides an insightful analysis of homogeneity, the superposigram
provides an insightful analysis of superposition.
2. THE CAMERA RESPONSE FUNCTION
The camera response function f may in general be modeled by
cascading two non-linear functions as shown in figure 1. In this
diagram, the photographic quantity (photoquantity) of light mea- sured by the sensor, is mapped into pixel space by a non-linear
dynamic range compression function and a uniform quantizer. We
call the first function the Range Compression Function because
most camera response functions, such as the familiar gamma map-
ping, are convex. In this paper we assume that this function is
monotonic and convex. The quantizer in turn maps the range
compressed photoquantities into discrete pixel values.
Fig. 1
: The completed camera model as used in the paper. Lightspace values collected
by the camera sensor elements are compressed with a non-linear function, and then
quantized to yield pixel values.
By assumption the Range Compression Function is monotonic.
Thus: photoquantities in the range
[q
1
, q
2
), q
1
< q
2
will result in
some pixel value p
1
; photoquantities in the range
[q
2
, q
3
), q
2
< q
3
,
will result in pixel value p
2
; with p
1
< p
2
. We then simplify our
analysis by approximating the Range Compression Function as be-
ing linear between quantization points, and by assuming that the
probability distribution of the measured photoquantities is uniform
in this range. Therefore, given pixel value p
x
, the maximum like-
lihood estimate of the original photoquantity
¯
q
x
is given by:
f
-1
(p
x
) = q
x
=
q
x
+ q
x
+1
2
,
(1)
where q
x
and q
x
+1
are lightspace quantization points for pixel
value p
x
and is an arbitrary nonzero scale factor.
3. SOLVING FOR THE INVERSE CAMERA RESPONSE
FUNCTION
Since the property of superposition holds with photoquantities, we
can form the following equation:
f
-1
(f (q
a
)) + f
-1
(f (q
b
)) = ^
q
c
.
(2)
By equation (1),
^
q
c
= ¯
q
a
+ ¯
q
b
and by our assumptions,
^
q
c
is the
maximum likelihood estimate of q
a
+q
b
given our prior knowledge
of f
(q
a
) and f (q
b
). We can now form the superposition equation:
f
-1
(f (¯
q
a
)) + f
-1
(f (¯
q
b
)) = f
-1
(f (¯
q
a
+ ¯
q
b
)) + ¯
Q
,
(3)
where
¯
Q
is the mean error due to quantization of
^
q
c
. Under the
assumptions stated, we can solve for f
-1
, i.e. the mapping from
pixel value p
x
to maximum likelihood (ML) photoquantities
¯
q
x
,
by minimizing the the following equation:
e
=
X
n
`f
-1
(p
a
[n]) + f
-1
(p
b
[n]) - f
-1
(p
c
[n])
´
2
,
(4)
where p
a
[n], p
b
[n], p
c
[n] are the n
th
pixels of three images taken
of a scene with constant exposure and three illumination permuta-
tions of two light sources in an otherwise dark environment. Pixel
values p
a
and p
b
are from images of the scene with each of the two
Fig. 2
: One of the many datasets used in the computation of the superposigram. Left-
most: Picture of Deconism Gallery with only the upper lights turned on. Middle:
Picture with only the lower lights turned on. Rightmost: Picture with both the upper
and lower lights turned on together.
light sources turned on independently. Pixel value p
c
is from the
image of the scene with both light sources turned on together, as
shown in Fig 2.
Since digital cameras output a finite range of discrete pixel
values, care must be taken when applying the assumptions made
at the ends of the camera's range where clipping occurs. In the re-
mainder of the paper, we will assume that the camera outputs pixel
values in the range
[0, 255] with clipping occurring at 0 and 255.
This is not always the case, but the modification to the analysis
under other conditions is very simple.
Using the proposed model, pixel values
0 and 255 are pro-
duced by the range of photoquantities
[0, q
1
) and [q
255
,
) respec-
tively. Since the range
[q
255
,
) is infinite in size, the assumption
that the distribution of photoquantities that produce a pixel value
of
255 will approach a uniform distribution over a finite number
of images will obviously not hold. A similar argument applies
for pixel value
0. We therefore do not try to solve for f
-1
(0) or
f
-1
(255), or equivalently, to solve for ¯
q
0
and
¯
q
255
can solve for the quantization points q
1
and q
255
. This allows us to
conclude that if we measure a pixel value of
0 with the camera, a
quantity of light below q
1
was measured. Similarly, a pixel value
of
255 represents a photoquantity greater than q
255
. The method
of solving for these thresholds is presented later in the paper.
In accordance with this development, we define f
-1
as the
mapping from pixel values
(1, 2, 3...254) to the maximum likely
photoquantities
q
1
,
¯
q
2
,
¯
q
3
...
¯
q
254
). We can now write equation 4
more simply as:
e
=
X
n,P
a
,P
b
P
c
=0,255
`¯q
p
a
[n]
+ ¯
q
p
b
[n]
- ¯
q
p
c
[n]
´
2
(5)
Equation (5) can be efficiently minimized using a singular
value decomposition (SVD). To do this, we represent f
-1
as a
vector f
-1
= [¯
q
1
,
¯
q
2
,
¯
q
3
...
¯
q
254
]
T
and we form a constraint matrix
A such that the n
th
row of the matrix corresponds to the the n
th
pixel in images p
a
, p
b
and p
c
. Each row has a
1 in columns a and
b,
-1 in column c and zeros in all other columns. In the n
th
row, a,
b and c correspond to pixel values p
a
[n], p
b
[n] and p
c
[n] respec-
tively. The least squares solution of the homogeneous equation:
Af
-1
= 0 is then obtained by obtaining the SVD of A = U V
and using the column of V corresponding to the smallest singular
value in
.
Solving for f
-1
by this method assumes that the error:
=
q
a
+ ¯
q
b
- ¯
q
c
has zero mean. Without noise, clipping at
255 can
create a problem by biasing the distribution of the measured pixel
values. With camera noise, this bias becomes very significant in
pixel ranges near both clipping points:
0 and 255. Also, as with all
least squares methods, outlier points can significantly perturb the
solution. Fig. 3
: Surface of the superposigraph (slenderized superposigram, where the slender-
ization occurs along the z-axis using maximum likelihood). The surface is given by
f
(a) + f (b) = ¯
f
(a + b) with f (a) in the x-axis, f (b) in the y-axis, ¯
f
(a + b) in
the z-axis.
With these considerations, the method is improved by robustly
estimating f
q
c
) by generating a histogram of the measured pixel
values of c for each additive combination of a and b. By assuming
that the normalized histogram is a reasonable approximation of
the actual probability distribution of c, we can use the peak of this
histogram
^
c
a
+b
as our best estimate of f
-1
(f (¯
q
a
+ ¯
q
b
)). Our
minimization problem thus becomes:
e
=
X
pairs{x,y}
N
{x,y}
`¯q
x
+ ¯
q
y
- ¯
q
^
c
x+y
´
2
(6)
Where N
{x,y}
is the number of instances of f
-1
(a) + f
-1
(b) =
f
-1
(c) in the dataset.
We call the collection of histograms for each additive com-
bination of pixels a Superposigram (histogram of superpositions).
For a digital camera with
256 pixel levels, its associated Super-
posigram will be a
256 × 256 × 256 array, with the first two di-
mensions being the pixel values in image P
a
and P
b
respectively,
and the third dimension containing the number of occurrences of
each pixel value for each
{a, b} combination. The Superposigram
representation is effective since we can easily compile information
from multiple image sets by simply adding the Superposigrams
produced by each set, thereby increasing the accuracy of our esti-
mate of f
q
c
). In our experiments, we have also smoothed each
histogram with a Gaussian kernel to improve the estimate of the
peak location in conditions where
{a, b} combinations are poorly
represented.
It is revealing to plot the histogram peaks in the Superposi-
gram as a surface in three dimensions
(x, y, z). Here x and y rep-
resent the
{p
a
, p
b
} combination and z represents f (¯
q
c
) (the peak
of the histogram). We call this slenderized version of the super-
posigram, a superposigraph. Since most cameras exhibit a non-
linear response, we expect this surface to be curved. See figure 3
for the Superposigram surface of a Nikon D1 digital camera.
4. TESTING THE NEW METHOD
To show that the method presented in this paper performs reliably,
random synthetic lightspace data was generated. To this data, a
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
50
100
150
200
250
Recovered Simulated Response Functions
Photoquantity
Pixel Value
Fig. 4
: Plots of five different recovered response functions, together with ground truth,
using synthetically generated data. The proposed algorithm was then used to recover
256 discrete points (plotted as various shaped points). Note that the recovered points
fall virtually on top of the original functions (plotted as solid lines).
C
1
continuous function was applied to the lightspace data, and the
result quantized into 256 imagespace/pixel values. Following this
procedure, Gaussian noise of standard deviation 10 was added to
the pixel values. The singular value decomposition method was
then used to recover the function using the described constraint
matrix where the equations were generated using the slenderized
superposigram. The results of this procedure on the synthetic data
are shown in figure 4. As can be seen in the figure, the high
quantity of noise added has significantly affected the recovered
response functions, however it demonstrates the stability of the al-
gorithm. When more reasonable noise levels are used, such as a
standard deviation of 3, the results are very accurate.
The algorithm was then tested on actual image data captured
by a Nikon D1 digital camera. The superposigraph shown in fig-
ure 3 was generated from the images presented in figure 2. To
create more data which would span the entire range of the camera
response function, several exposures f
(q
a
), f (q
b
) and f (q
a
+ q
b
)
were used. Though it is possible to generate good response func-
tions from one set of three images, using more image sets did in
fact produce a more reliable response function. From the super-
posigraph that was generated from the multiple image sets (we
used 10 in total), the response function for camera was determined.
To visualize how well the recovered camera estimated response
function did at linearization, the inverse camera response function
may be applied to the superposigraph. The linearized lightspace
surface arising from f
-1
(f (q
a
)) + f
-1
(f (q
b
)) = ^
f
-1
(f (q
a
+
q
b
)), is shown in figure 5
1
.
5. CONFIRMING THE CORRECTNESS OF THE
CAMERA RESPONSE FUNCTION
To test the accuracy of the recovered camera response functions,
two tests were devised. The following sections describe these tests,
after which our results are presented.
1
If it were not for the nonlinear nature of the response function, f , the
superposigram would be a plane plus noise in three dimensional space.
With the recovered camera response function, we can linearize the images
by converting them from imagespace, f
(q), into lightspace, q. Fig. 5
: We can visualize the efficacy of our method by looking at how well the in-
verse response function planarized the superposigram. The surface of f
-1
(f (a)) +
f
-1
(f (b)) = ^
f
-1
(f (a + b)) with f
-1
(f (a)) in the x-axis, f
-1
(f (b)) in the
y-axis, and ^
f
-1
(f (a + b)) in the z-axis.
5.1. Confirming the correctness of the camera response func-
tion by homogeneity
The first measure described is termed a homogeneity-test of the
camera response function (regardless of how it was obtained). The
homogeneity-test requires two differently (by a scalar factor of k)
exposed pictures, f
(q) and f (kq), of the same subject matter.
To conduct the test, the dark image f
(q) is lightened, and then
tested to see how close it is (in the mean squared error sense) to
f
(kq). The mean-squared difference is termed the homogeneity
error. To lighten the dark image, it is first converted from im-
agespace f to lightspace, q, by computing f
-1
(f (q)). Then the
photoquantities q bre multiplied by a constant value, k. Finally,
we convert it back to imagespace, by applying f . Alternatively we
could apply f
-1
to both images and multiply the first by k and
compare them in lightspace (as photoquantities).
5.2. Confirming the correctness of the camera response func-
tion by superposition
Another test of a camera response function termed the superposition-
test
, requires three pictures p
a
= f (q
a
), p
b
= f (q
b
) and p
c
=
f
(q
a
+b
). The inverse response function is applied to p
a
and p
b
and the resulting photoquantities q
a
and q
b
compare this sum (in either imagespace or lightspace) with p
c
(or
q
c
). The resulting mean squared difference is the superposition
error.
5.3. Comparing homogeneity and superposition errors in re-
sponse functions found by each of various methods
The results of comparison of homogeneity and superposition er-
rors in response functions found by various methods (including
previous published work) are compared in Table 1.
6. CONCLUSION
In this paper we showed how an unknown nonlinear camera re-
sponse function can be recovered using the superposition prop-
Method used to determine
Super-
Homo-
the response function
position
geneity
Error
Error
Homogeneity with parametric solution
(Previous Work )
8.8096
9.9827
Homogeneity, direct solution
8.6751
9.4011
Superposition, direct solution
8.5450
9.5361
Table 1
: This table shows the per-pixel errors observed in using lookup tables aris-
ing from several methods of calculating f and f
-1
. The leftmost column denotes
the method used to determine the response function. The middle column denotes
how well the resulting response function superimposes images, based on testing the
candidate response function on pictures of subject matter taken under different light-
ing positions. The rightmost column denotes how well the resulting response function
amplitude-scales images, and was determined based on using differently exposed pic-
tures of the same subject matter. The entries in the rightmost two columns are mean
squared error divided by the number of pixels in an image.
erty of light. As with earlier work using comparagrams where
improved results were obtained through slenderization of the com-
paragram, we found in this paper that improved results were ob-
tained through slenderization of the superposigram. The new method
was tested on various synthetic sequences with synthetic noise, to
prove ground-truth, as well as on actual data, to show that it works
in real world images as well.
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