Gradient Domain High Dynamic Range Compression
Raanan Fattal Dani Lischinski Michael Werman
School of Computer Science and Engineering
∗
The Hebrew University of Jerusalem
Abstract
We present a new method for rendering high dynamic range im-
ages on conventional displays. Our method is conceptually simple,
computationally efficient, robust, and easy to use. We manipulate
the gradient field of the luminance image by attenuating the mag-
nitudes of large gradients. A new, low dynamic range image is
then obtained by solving a Poisson equation on the modified gra-
dient field. Our results demonstrate that the method is capable of
drastic dynamic range compression, while preserving fine details
and avoiding common artifacts, such as halos, gradient reversals,
or loss of local contrast. The method is also able to significantly
enhance ordinary images by bringing out detail in dark regions.
CR Categories: I.3.3 [Computer Graphics]: Picture/image
generation—display algorithms, viewing algorithms; I.4.3 [Im-
age Processing and Computer Vision]: Enhancement—filtering,
grayscale manipulation, sharpening and deblurring
Keywords: digital photography, high dynamic range compression,
image-based rendering, image processing, signal processing, tone
mapping
1 Introduction
High dynamic range (HDR) radiance maps are becoming increas-
ingly common and important in computer graphics. Initially, such
maps originated almost exclusively from physically-based lighting
simulations. Today, however, HDR maps of real scenes are very
easy to construct: all you need is a few differently exposed pho-
tographs of the scene [Debevec and Malik 1997], or a panoramic
video scan of it [Aggarwal and Ahuja 2001a; Schechner and Nayar
2001]. Furthermore, based on recent developments in digital imag-
ing technology [Aggarwal and Ahuja 2001b; Nayar and Mitsunaga
2000], it is reasonable to assume that tomorrow’s digital still and
video cameras will capture HDR images and video directly.
HDR images have many advantages over standard low dynamic
range images [Debevec and Malik 1997], and several applications
have been demonstrated where such images are extremely use-
ful [Debevec 1998; Cohen et al. 2001]. However, HDR images also
pose a difficult challenge: given that the dynamic range of various
common display devices (monitors, printers, etc.) is much smaller
than the dynamic range commonly found in real-world scenes, how
can we display HDR images on low dynamic range (LDR) display
∗
e-mail: {raananf | danix | werman}@cs.huji.ac.il
devices, while preserving as much of their visual content as possi-
ble? This is precisely the problem addressed in this paper.
The problem that we are faced with is vividly illustrated by the
series of images in Figure 1. These photographs were taken using
a digital camera with exposure times ranging from 1/1000 to 1/4
of a second (at f/8) from inside a lobby of a building facing glass
doors leading into a sunlit inner courtyard. Note that each exposure
reveals some features that are not visible in the other photographs
1
.
For example, the true color of the areas directly illuminated by the
sun can be reliably assessed only in the least exposed image, since
these areas become over-exposed in the remainder of the sequence.
The color and texture of the stone tiles just outside the door are
best captured in the middle image, while the green color and the
texture of the ficus plant leaves becomes visible only in the very
last image in the sequence. All of these features, however, are si-
multaneously clearly visible to a human observer standing in the
same location, because of adaptation that takes place as our eyes
scan the scene [Pattanaik et al. 1998]. Using Debevec and Malik’s
method [1997], we can compile these 8-bit images into a single
HDR radiance map with dynamic range of about 25,000:1. How-
ever, it is not at all clear how to display such an image on a CRT
monitor whose dynamic range is typically below 100:1!
In this paper, we present a new technique for high dynamic range
compression that enables HDR images, such as the one described
in the previous paragraph, to be displayed on LDR devices. The
proposed technique is quite effective, as demonstrated by the top
image in Figure 2, yet it is conceptually simple, computationally
efficient, robust, and easy to use. Observing that drastic changes in
luminance across an HDR image must give rise to luminance gradi-
ents of large magnitudes, our approach is to manipulate the gradient
field of the luminance image by attenuating the magnitudes of large
gradients. A new, low dynamic range image is then obtained by
solving a Poisson equation on the modified gradient field.
The current work is not the first attempt to tackle this important
problem. Indeed, quite a few different methods have appeared in the
literature over the last decade. A more detailed review of previous
work is provided in Section 2. In this paper we hope to convince the
reader that our approach has some definite advantages over previous
solutions: it does a better job at preserving local contrasts than some
previous methods, has fewer visible artifacts than others, and yet it
is fast and easy to use. These advantages make our technique a
highly practical tool for high dynamic range compression.
We do not attempt to faithfully reproduce the response of the
human visual system to the original high dynamic range scenes.
Nevertheless, we have achieved very good results on HDR radiance
maps of real scenes, HDR panoramic video mosaics, ordinary high-
contrast photographs, and medical images.
2 Previous work
In the past decade there has been considerable work concerned with
displaying high dynamic range images on low dynamic range dis-
1
All of the images in this paper are provided at full resolution on the
proceedings CD-ROM (and also at http://www.cs.huji.ac.il/˜danix/hdrc), as
some of the fine details may be difficult to see in the printed proceedings.
Copyright © 2002 by the Association for Computing Machinery, Inc.
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249
Figure 1: A series of five photographs. The exposure is increasing from left (1/1000 of a second) to right (1/4 of a second).
play devices. In this section we provide a brief review of previous
work. More detailed and in-depth surveys are presented by DiCarlo
and Wandell [2001] and Tumblin et al. [1999].
Most HDR compression methods operate on the luminance
channel or perform essentially the same processing independently
in each of the RGB channels, so throughout most of this paper we
will treat HDR maps as (scalar) luminance functions.
Previous approaches can be classified into two broad groups: (1)
global (spatially invariant) mappings, and (2) spatially variant op-
erators. DiCarlo and Wandell [2001] refer to the former as TRCs
(tone reproduction curves) and to the latter as TROs (tone repro-
duction operators); we adopt these acronyms for the remainder of
this paper.
The most naive TRC linearly scales the HDR values such that
they fit into a canonic range, such as [0,1]. Such scaling preserves
relative contrasts perfectly, but the displayed image may suffer se-
vere loss of visibility whenever the dynamic range of the display
is smaller than the original dynamic range of the image, and due
to quantization. Other common TRCs are gamma correction and
histogram equalization.
In a pioneering work, Tumblin and Rushmeier [1993] describe a
more sophisticated non-linear TRC designed to preserve the appar-
ent brightness of an image based on the actual luminances present in
the image and the target display characteristics. Ward [1994] sug-
gested a simpler linear scale factor automatically determined from
image luminances so as to preserve apparent contrast and visibil-
ity around a particular adaptation level. The most recent and most
sophisticated, to our knowledge, TRC is described by Ward Larson
et al. [1997]. They first describe a clever improvement to histogram
equalization, and then show how to extend this idea to incorporate
models of human contrast sensitivity, glare, spatial acuity, and color
sensitivity effects. This technique works very well on a wide variety
of images.
The main advantage of TRCs lies in their simplicity and compu-
tational efficiency: once a mapping has been determined, the image
may be mapped very quickly, e.g., using lookup tables. However,
such global mappings must be one-to-one and monotonic in order to
avoid reversals of local edge contrasts. As such, they have a funda-
mental difficulty preserving local contrasts in images where the in-
tensities of the regions of interest populate the entire dynamic range
in a more or less uniform fashion. This shortcoming is illustrated
in the middle image of Figure 2. In this example, the distribution of
luminances is almost uniform, and Ward Larson’s technique results
in a mapping, which is rather similar to a simple gamma correction.
As a result, local contrast is drastically reduced.
Spatially variant tone reproduction operators are more flexible
than TRCs, since they take local spatial context into account when
deciding how to map a particular pixel. In particular, such operators
can transform two pixels with the same luminance value to different
display luminances, or two different luminances to the same display
intensity. This added flexibility in the mapping should make it pos-
sible to achieve improved local contrast.
The problem of high-dynamic range compression is intimately
related to the problem of recovering reflectances from an image
[Horn 1974]. An image I(x,y) is regarded as a product
I(x,y)=R(x, y) L(x,y),
where R(x, y) is the reflectance and L(x, y) is the illuminance at each
point (x,y). The function R(x , y) is commonly referred to as the
intrinsic image of a scene. The largest luminance variations in an
HDR image come from the illuminance function L, since real-world
reflectances are unlikely to create contrasts greater than 100:1
2
.
Thus, dynamic range compression can, in principle, be achieved
by separating an image I to its R and L components, scaling down
the L component to obtain a new illuminance function
˜
L, and re-
multiplying:
˜
I(x,y)=R(x, y)
˜
L(x,y).
Intuitively, this reduces the contrast between brightly illuminated
areas and those in deep shadow, while leaving the contrasts due to
texture and reflectance undistorted. Tumblin et al. [1999] use this
approach for displaying high-contrast synthetic images, where the
material properties of the surfaces and the illuminance are known
at each point in the image, making it possible to compute a per-
fect separation of an image to various layers of lighting and surface
properties.
Unfortunately, computing such a separation for real images is
an ill posed problem [Ramamoorthi and Hanrahan 2001]. Conse-
quently, any attempt to solve it must make some simplifying as-
sumptions regarding R, L, or both. For example, homomorphic
filtering [Stockham 1972], an early image enhancement technique,
makes the assumption that L varies slowly across the image, in con-
trast to R that varies abruptly. This means that R can be extracted by
applying a high-pass filter to the logarithm of the image. Exponenti-
ating the result achieves simultaneous dynamic range compression
and local contrast enhancement. Similarly, Horn [1974] assumes
that L is smooth, while R is piecewise-constant, introducing infi-
nite impulse edges in the Laplacian of the image’s logarithm. Thus,
L may be recovered by thresholding the Laplacian. Of course, in
most natural images the assumptions above are violated: for ex-
ample, in sunlit scenes illuminance varies abruptly across shadow
boundaries. This means that L also has high frequencies and intro-
duces strong impulses into the Laplacian. As a result, attenuating
only the low frequencies in homomorphic filtering may give rise
to strong “halo” artifacts around strong abrupt changes in illumi-
nance, while Horn’s method incorrectly interprets sharp shadows
as changes in reflectance.
More recently, Jobson et al. [1997] presented a dynamic range
compression method based on a multiscale version of Land’s
“retinex” theory of color vision [Land and McCann 1971]. Retinex
estimates the reflectances R(x, y) as the ratio of I(x,y) to its low-
pass filtered version. A similar operator was explored by Chiu
et al. [1993], and was also found to suffer from halo artifacts and
dark bands around small bright visible light sources. Jobson et al.
compute the logarithm of the retinex responses for several low-pass
filters of different sizes, and linearly combine the results. The lin-
ear combination helps reduce halos, but does not eliminate them
entirely. Schlick [1994] and Tanaka and Ohnishi [1997] also exper-
imented with spatially variant operators and found them to produce
halo artifacts.
Pattanaik and co-workers [1998] describe an impressively com-
prehensive computational model of human visual system adaptation
2
For example, the reflectance of black velvet is about 0.01, while that of
snow is roughly 0.93.
250
Figure 2: Belgium House: An HDR radiance map of a lobby com-
pressed for display by our method (top), the method of Ward Larson
et al. (middle) and the LCIS method (bottom).
and spatial vision for realistic tone reproduction. Their model en-
ables display of HDR scenes on conventional display devices, but
the dynamic range compression is performed by applying different
gain-control factors to each bandpass, which also results in halos
around strong edges. In fact, DiCarlo and Wandell [2001], as well
as Tumblin and Turk [1999] demonstrate that this is a fundamental
problem with any multi-resolution operator that compresses each
resolution band differently.
In order to eradicate the notorious halo artifacts Tumblin and
Turk [1999] introduce the low curvature image simplifier (LCIS) hi-
erarchical decomposition of an image. Each level in this hierarchy
is generated by solving a partial differential equation inspired by
anisotropic diffusion [Perona and Malik 1990] with a different dif-
fusion coefficient. The hierarchy levels are progressively smoother
versions of the original image, but the smooth (low-curvature) re-
gions are separated from each other by sharp boundaries. Dynamic
range compression is achieved by scaling down the smoothest ver-
sion, and then adding back the differences between successive lev-
els in the hierarchy, which contain details removed by the simpli-
fication process. This technique is able to drastically compress the
dynamic range, while preserving the fine details in the image. How-
ever, the results are not entirely free of artifacts. Tumblin and Turk
note that weak halo artifacts may still remain around certain edges
in strongly compressed images. In our experience, this technique
sometimes tends to overemphasize fine details. For example, in the
bottom image of Figure 2, generated using this technique, certain
features (door, plant leaves) are surrounded by thin bright outlines.
In addition, the method is controlled by no less than 8 parameters,
so achieving an optimal result occasionally requires quite a bit of
trial-and-error. Finally, the LCIS hierarchy construction is compu-
tationally intensive, so compressing a high-resolution image takes
a substantial amount of time.
3 Gradient domain HDR compression
Informally, our approach relies on the widely accepted assumptions
[DiCarlo and Wandell 2001] that the human visual system is not
very sensitive to absolute luminances reaching the retina, but rather
responds to local intensity ratio changes and reduces the effect of
large global differences, which may be associated with illumination
differences.
Our algorithm is based on the rather simple observation that any
drastic change in the luminance across a high dynamic range im-
age must give rise to large magnitude luminance gradients at some
scale. Fine details, such as texture, on the other hand, correspond
to gradients of much smaller magnitude. Our idea is then to iden-
tify large gradients at various scales, and attenuate their magnitudes
while keeping their direction unaltered. The attenuation must be
progressive, penalizing larger gradients more heavily than smaller
ones, thus compressing drastic luminance changes, while preserv-
ing fine details. A reduced high dynamic range image is then re-
constructed from the attenuated gradient field.
It should be noted that all of our computations are done on the
logarithm of the luminances, rather than on the luminances them-
selves. This is also the case with most of the previous methods
reviewed in the previous section. The reason for working in the log
domain is twofold: (a) the logarithm of the luminance is a (crude)
approximation to the perceived brightness, and (b) gradients in the
log domain correspond to ratios (local contrasts) in the luminance
domain.
We begin by explaining the idea in 1D. Consider a high dynamic
range 1D function. We denote the logarithm of this function by
H(x). As explained above, our goal is to compress large magnitude
changes in H, while preserving local changes of small magnitude,
as much as possible. This goal is achieved by applying an appro-
priate spatially variant attenuating mapping Φ to the magnitudes of
the derivatives H
(x). More specifically, we compute:
G(x)=H
(x) Φ(x).
Note that G has the same sign as the original derivative H
every-
where, but the magnitude of the original derivatives has been al-
tered by a factor determined by Φ, which is designed to attenuate
large derivatives more than smaller ones. Actually, as explained in
Section 4, Φ accounts for the magnitudes of derivatives at different
scales.
251
(a) (b) (c) (d) (e) (f)
Figure 3: (a) An HDR scanline with dynamic range of 2415:1. (b) H(x)=log(scanline). (c) The derivatives H
(x). (d) Attenuated derivatives
G(x); (e) Reconstructed signal I(x ) (as defined in eq. 1); (f) An LDR scanline exp(I(x)): the new dynamic range is 7.5:1. Note that each plot
uses a different scale for its vertical axis in order to show details, except (c) and (d) that use the same vertical axis scaling in order to show
the amount of attenuation applied on the derivatives.
We can now reconstruct a reduced dynamic range function I (up
to an additive constant C) by integrating the compressed derivatives:
I(x)=C +
x
0
G(t) dt. (1)
Finally, we exponentiate in order to return to luminances. The entire
process is illustrated in Figure 3.
In order to extend the above approach to 2D HDR functions
H(x, y) we manipulate the gradients ∇H, instead of the derivatives.
Again, in order to avoid introducing spatial distortions into the im-
age, we change only the magnitudes of the gradients, while keeping
their directions unchanged. Thus, similarly to the 1D case, we com-
pute
G(x,y)=∇H(x , y) Φ(x,y).
Unlike the 1D case we cannot simply obtain a compressed dynamic
range image by integrating G, since it is not necessarily integrable.
In other words, there might not exist an image I such that G = ∇I!
In fact, the gradient of a potential function (such as a 2D image)
must be a conservative field [Harris and Stocker 1998]. In other
words, the gradient ∇I =(
∂
I/
∂
x,
∂
I/
∂
y) must satisfy
∂
2
I
∂
x
∂
y
=
∂
2
I
∂
y
∂
x
,
which is rarely the case for our G.
One possible solution to this problem is to orthogonally project
G onto a finite set of orthonormal basis functions spanning the set of
integrable vector fields, such as the Fourier basis functions [Frankot
and Chellappa 1988]. In our method we employ a more direct and
more efficient approach: search the space of all 2D potential func-
tions for a function I whose gradient is the closest to G in the least-
squares sense. In other words, I should minimize the integral
F(∇I, G) dx dy, (2)
where F(∇I,G)=
∇I − G
2
=
∂
I
∂
x
− G
x
2
+
∂
I
∂
y
− G
y
2
.
According to the Variational Principle, a function I that mini-
mizes the integral in (2) must satisfy the Euler-Lagrange equation
∂
F
∂
I
−
d
dx
∂
F
∂
I
x
−
d
dy
∂
F
∂
I
y
= 0,
which is a partial differential equation in I. Substituting F we obtain
the following equation:
2
∂
2
I
∂
x
2
−
∂
G
x
∂
x
+ 2
∂
2
I
∂
y
2
−
∂
G
y
∂
y
= 0.
Dividing by 2 and rearranging terms, we obtain the well-known
Poisson equation
∇
2
I = divG (3)
Figure 4: Gradient attenuation factors used to compress the Bel-
gium House HDR radiance map (Figure 2). Darker shades indicate
smaller scale factors (stronger attenuation).
where ∇
2
is the Laplacian operator ∇
2
I =
∂
2
I
∂
x
2
+
∂
2
I
∂
y
2
and divG is the
divergence of the vector field G, defined as divG =
∂
G
x
∂
x
+
∂
G
y
∂
y
. This
is a linear partial differential equation, whose numerical solution is
described in Section 5.
4 Gradient attenuation function
As explained in the previous section, our method achieves HDR
compression by attenuating the magnitudes of the HDR image gra-
dients by a factor of Φ(x, y) at each pixel. We would like the at-
tenuation to be progressive, shrinking gradients of large magnitude
more than small ones.
Real-world images contain edges at multiple scales. Conse-
quently, in order to reliably detect all of the significant inten-
sity transitions we must employ a multi-resolution edge detection
scheme. However, we cannot simply attenuate each gradient at the
resolution where it was detected. This could result in halo artifacts
around strong edges, as mentioned in Section 2. Our solution is to
propagate the desired attenuation from the level it was detected at
to the full resolution image. Thus, all gradient manipulations occur
at a single resolution level, and no halo artifacts arise.
We begin by constructing a Gaussian pyramid H
0
,H
1
,...,H
d
,
where H
0
is the full resolution HDR image and H
d
is the coarsest
level in the pyramid. d is chosen such that the width and the height
of H
d
are at least 32. At each level k we compute the gradients
using central differences:
∇H
k
=
H
k
(x + 1, y) − H
k
(x − 1, y)
2
k+1
,
H
k
(x,y + 1)− H
k
(x,y − 1)
2
k+1
.
At each level k a scaling factor
ϕ
k
(x,y) is determined for each pixel
252
based on the magnitude of the gradient there:
ϕ
k
(x,y)=
α
∇H
k
(x,y)
∇H
k
(x,y)
α
β
.
This is a two-parameter family of functions. The first parameter
α
determines which gradient magnitudes remain unchanged (mul-
tiplied by a scale factor of 1). Gradients of larger magnitude are
attenuated (assuming
β
< 1), while gradients of magnitude smaller
than
α
are slightly magnified. In all the results shown in this pa-
per we set
α
to 0.1 times the average gradient magnitude, and
β
between 0.8 and 0.9.
The full resolution gradient attenuation function Φ(x, y) is com-
puted in a top-down fashion, by propagating the scaling factors
ϕ
k
(x,y) from each level to the next using linear interpolation and
accumulating them using pointwise multiplication. More formally,
the process is given by the equations:
Φ
d
(x,y)=
ϕ
d
(x,y)
Φ
k
(x,y)=L
Φ
k+1
(x,y)
ϕ
k
(x,y)
Φ(x,y)=Φ
0
(x,y)
where d is the coarsest level, Φ
k
denotes the accumulated attenua-
tion function at level k, and L is an upsampling operator with linear
interpolation. As a result, the gradient attenuation at each pixel
of the finest level is determined by the strengths of all the edges
(from different scales) passing through that location in the image.
Figure 4 shows attenuation coefficients computed for the Belgium
House HDR radiance map.
It is important to note that although the computation of the gra-
dient attenuation function is done in a multi-resolution fashion, ul-
timately only the gradients at the finest resolution are manipulated,
thus avoiding halo artifacts that typically arise when different reso-
lution levels are manipulated separately.
5 Implementation
In order to solve a differential equation such as (3) one must first
specify the boundary conditions. In our case, the most natural
choice appears to be the Neumann boundary conditions ∇I · n = 0
(the derivative in the direction normal to the boundary is zero). With
these boundary conditions the solution is now defined up to a single
additive term, which has no real meaning since we shift and scale
the solution in order to fit it into the display device limits.
Since both the Laplacian ∇
2
and div are linear operators, approx-
imating them using standard finite differences yields a linear system
of equations. More specifically, we approximate:
∇
2
I(x,y) ≈ I(x+1,y)+I(x−1,y)+I(x,y+1)+I(x,y−1)−4I(x,y)
taking the pixel grid spacing to be 1 at the full resolution of the im-
age. The gradient ∇H is approximated using the forward difference
∇H(x, y) ≈ (H(x + 1, y) − H(x,y),H(x,y + 1) − H(x,y)),
while for divG we use backward difference approximations
divG ≈ G
x
(x,y) − G
x
(x − 1, y)+G
y
(x,y) − G
y
(x,y − 1).
This combination of forward and backward differences ensures that
the approximation of divG is consistent with the central difference
scheme used for the Laplacian.
At the boundaries we use the same definitions, but assume that
the derivatives around the original image grid are 0. For example,
for each pixel on the left image boundary we have the equation
I(−1,y) − I(0,y)=0.
The finite difference scheme yields a large system of linear equa-
tions — one for each pixel in the image, but the corresponding ma-
trix has only five nonzero elements in each row, since each pixel is
coupled only with its four neighbors. We solve this system using
the Full Multigrid Algorithm [Press et al. 1992], with Gauss-Seidel
smoothing iterations. This leads to O(n) operations to reach an ap-
proximate solution, where n is the number of pixels in the image.
Another alternative is to use a “rapid Poisson solver”, which uses
the fast Fourier transform to invert the Laplacian operator. How-
ever, the complexity with this approach would be O(nlogn).
As mentioned earlier, our method operates on the luminances of
an HDR radiance map. In order to assign colors to the pixels of
the compressed dynamic range image we use an approach similar
to those of Tumblin and Turk [1999] and Schlick [1994]. More
specifically, the color channels of a pixel in the compressed dy-
namic range image are computed as follows:
C
out
=
C
in
L
in
s
L
out
for C = R,G,B. L
in
and L
out
denote the luminance before and after
HDR compression, respectively, and the exponent s controls the
color saturation of the resulting image. We found values between
0.4 and 0.6 to produce satisfactory results.
6 Results
Multiple exposure HDRs. We have experimented with our
method on a variety of HDR radiance maps of real scenes. In all
cases, our method produced satisfactory results without much pa-
rameter tweaking. In certain cases we found that the subjective
quality of the resulting image is slightly enhanced by running a
standard sharpening operation. The computation times range from
1.1 seconds for an 512 by 384 image to 4.5 seconds for an 1024 by
768 image on a 1800 MHz Pentium 4.
The top row in Figure 5 shows three different renderings of a
“streetlight on a foggy night” radiance map
3
. The dynamic range
in this scene exceeds 100,000:1. The left image was produced us-
ing the method of Ward Larson et al. [1997], and the right image
4
was produced by Tumblin and Turk’s [1999] LCIS method. The
middle image was generated by our method. The left image loses
visibility in a wide area around the bright light, details are lost in
the shadowed regions, and the texture on the ground is washed
out. The LCIS image (right) exhibits a grainy texture in smooth
areas, and appears to slightly overemphasize edges, resulting in an
“embossed”, non-photorealistic appearance. In our image (middle)
smoothness is preserved in the foggy sky, yet at the same time fine
details are well preserved (tree leaves, ground texture, car outlines).
Our method took 5 seconds to compute this 751 by 1130 image,
while the LCIS method took around 8.5 minutes.
The second row of images in Figure 5 shows a similar compari-
son using an HDR radiance map of the Stanford Memorial church
5
.
The dynamic range in this map exceeds 250,000:1. Overall, the
same observations as before hold for this example as well. In the
left image the details in the dark regions are difficult to see, while
the skylight and the stained glass windows appear over-exposed. In
the LCIS image (right) the floor appears slightly bumpy, while our
image (middle) shows more details and conveys a more realistic
impression.
The last row of images
6
in Figure 5 and the first row in Fig-
ure 6 show several additional examples of HDR compression by
3
Radiance map courtesy of Jack Tumblin, Northwestern University.
4
Image reprinted by permission,
c
1999 Jack Tumblin [1999].
5
Radiance map courtesy of Paul Debevec.
6
Source exposures courtesy of Shree Nayar.
253
