Exposure Fusion
Tom Mertens1

Jan Kautz2

Frank Van Reeth1

1Hasselt University — EDM
transationale Universiteit Limburg
Belgium

2

University College London
UK

Abstract
We propose a technique for fusing a bracketed exposure
sequence into a high quality image, without converting to
HDR first. Skipping the physically-based HDR assembly
step simplifies the acquisition pipeline. This avoids camera
response curve calibration and is computationally efficient.
It also allows for including flash images in the sequence.
Our technique blends multiple exposures, guided by simple
quality measures like saturation and contrast. This is donein a multiresolution fashion to account for the brightness
variation in the sequence. The resulting image quality iscomparable to existing tone mapping operators.

(a) Exposure bracketed sequence

1. Introduction
Digital cameras have a limited dynamic range, which islower than one encounters in the real world. In high dynamic range scenes, a picture will often turn out to be underor overexposed. A bracketed exposure sequence [5, 17, 26]
allows for acquiring the full dynamic range, and can beturned into a single high dynamic range image. Upon display, the intensities need to be remapped to match the typically low dynamic range of the display device, through aprocess called tone mapping [26].
In this paper, we propose to skip the step of computing ahigh dynamic range image, and immediately fuse the multiple exposures into a high-quality, low dynamic range image,ready for display (like a tone-mapped picture). We call thisprocess exposure fusion; see Fig. 1. The idea behind our approach is that we compute a perceptual quality measure foreach pixel in the multi-exposure sequence, which encodes
desirable qualities, like saturation and contrast. Guided byour quality measures, we select the “good” pixels from thesequence and combine them into the final result.
Exposure fusion is similar to other image fusion techniques for depth-of-field extension [19] and photomontage [1]. Burt et al. [4] have proposed the idea of fusing a(b) Fused result

Figure 1. Demonstration of exposure fusion. A
multi-exposure sequence is assembled directly into a high quality image, without converting to HDR first. No camera-specificknowledge, such as the response curve, had
to be accounted for. Total processing time
was only 3.3 seconds (1 megapixel). Image
courtesy of Jacques Joffre.

multi-exposure sequence, but in the context of general image fusion. We introduce a method that can more easily
incorporate desired image qualities, in particular those thatare relevant for combining different exposures.
Exposure fusion has several advantages. First of all,the acquisition pipeline is simplified, no in-between HDR
image needs to be computed. Since our technique is not

(a) Input images with corresponding weight maps(b) Fused result

Figure 2. Exposure fusion is guided by weight maps for each input image. A high weight means thata pixel should appear in the final image. These weights reflect desired image qualities, such as highcontrast and saturation. Image courtesy of Jacques Joffre.

physically-based, we do not need to worry about calibration of the camera response curve, and keeping track ofeach photograph’s exposure time. We can even add a flash
image to the sequence to enrich the result with additional
detail. Our approach merely relies on simple quality measures, like saturation and contrast, which prove to be very
effective. Also, results can be computed at near-interactive
rates, as our technique mostly relies a pyramidal image decomposition. On the downside, we cannot extend the dynamic range of the original pictures, but instead we directly
produce a well-exposed image for display purposes.

2. Related WorkHigh dynamic range (HDR) imaging assembles a highdynamic range image from a set of low dynamic range images that were acquired with a normal camera [5, 17]. The
camera-specific response curve should be recovered in order to linearize the intensities. This calibration step can becomputed from the input sequence and their exposure settings.
Most display devices have a limited dynamic range andcannot directly display HDR images. To this end, tone
mapping compresses the dynamic range to fit the dynamic
range of the display device [26]. Many different tone mapping operators have been suggested with different advantages and disadvantages. Global operators apply a spatially uniform remapping of intensity to compress the dynamic range [7, 14, 24]. Their main advantage is speed,
but sometimes fail to reproduce a pleasing image. Local
tone mapping operators apply a spatially varying remapping [6, 8, 10, 15, 25, 29], i.e., the mapping changes for different regions in the image. This often yields more pleasing
images, even though the result may look unnatural sometimes. The operators employ very different techniques tocompress the dynamic range: from bilateral filtering [8],which decomposes the image into edge-aware low and highfrequency components, to compression in the gradient domain [10]. The following two local operators are related
to our method. Reinhard et al. [25] compute a multi-scalemeasure that is related to contrast and rescales the HDR
pixel values accordingly. This is in a way similar to ourmeasures. However, our measures are solely defined per
pixel. The method by Li et al. [15] uses a pyramidal image decomposition, and attenuate the coefficients at eachlevel to compress the dynamic range. Our method is also
pyramid-based, but it works on the coefficients of the different exposures instead of those of an in-between HDR
image. Other tone mappers try to mimic the human visualsystem, e.g., to simulate temporal adaptation [20]. Instead,
we aim at creating pleasing images and try to reproduce asmuch detail and color as possible.
Image fusion techniques have been used for many years.
For example, for depth-of-field enhancement [19, 13], multimodal imaging [4], and video enhancement [23]. We
will use image fusion for creating a high quality image
from bracketed exposures. In the early 90’s, Burt et al. [4]have already proposed to use image fusion in this context.
However, our method is more flexible by incorporating adjustable image measures, such as contrast and saturation.
Goshtasby [11] also proposed a method to blend multiple
exposures, but it cannot deal well with object boundaries. A
more thorough discussion of these techniques is presented
in Sec. 3.3.
Grundland et al. [12] cross-dissolve between two images
using a pyramid decomposition [3]. We use a similar blending strategy, but employ different quality measures.
We demonstrate that our technique can be used as a simple way to fuse flash/no-flash images. Previous techniques
for this are much more elaborate [9, 2] and are specifi-cally designed for this case, whereas our method is flexible
enough to handle that case as well.

3. Exposure Fusion

selection, resp.). Similar to weighted terms of a linear combination, we can control the influence of each measure using
a power function:
Wij,k = (Cij,k )ωC × (Sij,k )ωS × (Eij,k )ωE

Exposure fusion computes the desired image by keeping
only the “best” parts in the multi-exposure image sequence.
This process is guided by a set of quality measures, which
we consolidate into a scalar-valued weight map (see Fig. 2).
It is useful to think of the input sequence as a stack of images. The final image is then obtained by collapsing thestack using weighted blending.
As with previous HDR acquisition approaches [17, 5],
we assume that the images are perfectly aligned, possiblyusing a registration algorithm [30].

with C, S and E, being contrast, saturation, and wellexposedness, resp., and corresponding “weighting” exponents ωC , ωs , and ωE . The subscript ij, k refers to pixel(i, j) in the k-th image. If an exponent ω equals 0, the corresponding measure is not taken into account. The final pixel
weight Wij,k will be used to guide the fusion process, discussed in the next section. See Fig. 2 for an example ofweight maps.

3.1. Quality Measures

We will compute a weighted average along each pixel tofuse the N images, using weights computed from our quality measures. To obtain a consistent result, we normalize
the values of the N weight maps such that they sum to one
at each pixel (i, j):

Many images in the stack contain flat, colorless regions
due to under- and overexposure. Such regions should receive less weight, while interesting areas containing brightcolors and details should be preserved. We will use the following measures to achieve this:• Contrast: we apply a Laplacian filter to the grayscaleversion of each image, and take the absolute value ofthe filter response [16]. This yields a simple indicator
C for contrast. It tends to assign a high weight to important elements such as edges and texture. A similar
measure was used for multi-focus fusion for extended
depth-of-field [19].
• Saturation: As a photograph undergoes a longer exposure, the resulting colors become desaturated andeventually clipped. Saturated colors are desirable andmake the image look vivid. We include a saturation
measure S, which is computed as the standard deviation within the R, G and B channel, at each pixel.
• Well-exposedness: Looking at just the raw intensities
within a channel, reveals how well a pixel is exposed.
We want to keep intensities that are not near zero (underexposed) or one (overexposed). We weight each intensity i based on how close it is to 0.5 using a Gauss2
curve: exp − (i−0.5), where σ equals 0.2 in our im2σ 2plementation. To account for multiple color channels,
we apply the Gauss curve to each channel separately,
and multiply the results, yielding the measure E.
For each pixel, we combine the information from the different measures into a scalar weight map using multiplication. We opted for a product over a linear combination, as
we want to enforce all qualities defined by the measures atonce (i.e. like an “AND” selection, as opposed to an “OR”

3.2. FusionŴij,k =N
X

Wij,k′

−1

Wij,k

k′ =1

The resulting image R can then be obtained by aweighted blending of the input images:
Rij =

N
X

Ŵij,k Iij,k

(1)

k=1

with Ik the k-th input image in the sequence. Unfortunately, just applying Eq. 1 produces an unsatisfactory result. Wherever weights vary quickly, disturbing seams willappear (Fig. 4b). This happens because the images we are
combining, contain different absolute intensities due to theirdifferent exposure times. We could avoid sharp weight map
transitions by smoothing the weight map with a Gaussian
filter, but this results in undesirable halos around edges, andspills information across object boundaries (Fig. 4c). An
edge-aware smoothing operation using the cross-bilateralfilter seems like a better alternative [22, 9]. However, it isunclear how to define the control image, which would tell
us where the smoothing should be stopped. Using the original grayscale image as control image does not work well,
as demonstrated in Fig. 4d. Also, it is hard to find good parameters for the cross-bilateral filter (i.e., for controlling the
spatial and intensity influence).
To address the seam problem, we use a technique inspired by Burt and Adelson [3]. Their original techniqueseamlessly blends two images, guided by an alpha mask,
and works at multiple resolutions using a pyramidal image
decomposition. First, the input images are decomposed intoInput Images

Image - Laplacian Pyramid

Weight Map - Gaussian Pyramid*

Fused Pyramid

Final Image+*
Figure 3. We fuse differently exposed images using a Laplacian decomposition of the images and a
Gaussian pyramid of the weight maps, which represent measures such as contrast and saturation.
Image courtesy of Jacques Joffre.

a Laplacian pyramid, which basically contains band-passfiltered versions at different scales [3]. Blending is then carried out for each level separately. We adapt the techniqueto our case, where we have N images and N normalized
weight maps that act as alpha masks. Let the l-th level in a
Laplacian pyramid decomposition of an image A be definedas L{A}l , and G{B}l for a Gaussian pyramid of image B.
Then, we blend the coefficients (pixel intensities in the different pyramid levels) in a similar fashion to Eq. 1:
L{R}lij =N
X

G{Ŵ }lij,k L{I}lij,kk=1I.e., each level l of the resulting Laplacian pyramid is computed as a weighted average of the original Laplacian decompositions for level l, with the l-th level of Gaussian
pyramid of the weight map serving as the weights. Finally,
the pyramid L{R}l is collapsed to obtain R. An overview
of our technique is given in Figure 3.
Multiresolution blending is quite effective at avoiding
seams (Fig. 4), because it blends image features instead ofintensities. Since the blending equation (1) is computed ateach scale separately, sharp transitions in the weight map
can only affect sharp transitions appear in the original images (e.g. edges). Conversely, flat regions in the original
images will always have negligible coefficient magnitude,
and are thus not affected by possibly sharp variations in theweight function, even though the absolute intensities among
the inputs could be different there.
For dealing with color images, we have found that carrying out the blending each color channel separately produces
good results.

3.3. Discussion
Seamless blending is an often-encountered problem inimage processing and graphics. We use a multiresolution
technique based on image pyramids [3], but other methods

are available as well. In particular, gradient-based blending [21] has shown to be effective, and it has been applied
to image fusion as well [1, 23]. Gradient methods convert
images to gradient fields first, apply the blending operation,and reconstruct the final image from the resulting gradients.
However, reconstruction requires solving a partial differential equation, which can be costly for high resolution images. In addition, gradient-based fusion incurs a scale andshift ambiguity, and is prone to color shifting [23].
Tone mapping operators may also cause color shifts
like oversaturation [15], and possibly reduce contrast (seeFig. 7). Our blending is robust against changes in appearance, as it can be seen as a selection process. Even though
we select based on contrast and saturation, we do not directly change pixels to meet these constraints.
Our work bears similarity to early work on image fusion,
where the Laplacian (or another) pyramid decomposition isused as well [19, 28, 4]. These methods work directly on
the coefficients by retaining only those pyramid coefficientthat are most salient. For instance, the coefficients with thelargest magnitude are kept [19]:L{R}lij = argmax |L{I}lij,k |L{I}lij,kBurt and Kolczynski’s exposure fusion technique [4]is based on the same principle. These approaches compound all details present in the sequence, but they do notnecessarily produce an appealing result; see Fig. 5. Instead, we blend the pyramid coefficients based on a scalar
weight map, but do not directly process individual coefficients at different levels. Measures like saturation and wellexposedness are hard to evaluate directly from pyramid coefficients. Our technique basically decouples the weightingfrom the actual pyramid contents, which enables us to moreeasily define quality measures. In fact, any measure that canbe computed per-pixel, or perhaps in a very small neighborhood, is applicable.
Goshtasby’s technique [11] selects the optimal exposure

(a) input sequence(a) Input sequence(b) Naive(c) Blurred(b) Ogden et al. [19](d) Cross-Bilateral(e) Multiresolution

Figure 4. Weighted blending. The input sequence is shown in (a). Naive per-pixel
blending (b) yields obvious seams due tosharp variations in the weight map. Blurring the weight map using a Gaussian kernel(c) removes the seams, but introduces halosaround edges. Cross-bilateral filtering (d) isable to avoid some of the halos, but not all.
Multiresolution blending (e) creates the desired result.

on a per-block basis, and smoothly blends between blocks.
Since blocks may span across different objects, spill information across object boundaries, similar to the artifacts related to Gaussian blurring of the weight map (Fig. 4c).

4. Results
All of our examples were constructed from JPG-encoded
photographs, with unknown gamma correction and camera
response curve. We used equally weighted quality measures(ωC = ωS = ωE = 1) in most examples, except wherementioned otherwise.

4.1. Quality
Fig. 1 and 2 show a typical bracketed exposure shot:
underexposed, normally exposed and overexposed. Every
exposure contains relevant information that is not present(c) Burt et al. [4](d) Our techniqueFigure 5. Comparison with other pyramidbased fusion techniques [19, 4]. These methods select the most salient Laplacian pyramid coefficients in the input sequence (a),whereas our technique does blending. The
results (b,c) are too dark, and exhibit color
shifts. Our technique (e) produces a morefaithful result compared to the input sequence (a). Image courtesy of Jesse Levinson.

in the other exposures. Our technique is able to preservefinescale details of the buildings, and the warm appearanceof the sky.
In Fig. 7 and 9, we compare our result to tone mapping. A rigorous comparison is hard, due to the operators’
implementation-specific differences and parameter settings.
We therefore limit ourselves to an informal comparison witha few popular tone mappers. Compared to Durand et al. [8]and Reinhard et al. [25], our method offers better contrast.
Li et al.’s approach [15] produces quite similar results toours in terms of contrast, but it also exhibits slight oversaturation. We had to tweak the saturation parameter in theirimplementation to correct the colors.
The multiresolution blending technique discussed inSec. 3.2 is not without its problems. In Fig. 6, our resultcontains a spurious low frequency brightness change, which
is not present in the original image set. It is caused by ahighly varying change in brightness among the different exposures. Intuitively speaking, this artifact can be considered
as a very blurred version of the seam problem, illustrated
in Fig. 4b. Constructing a higher Laplacian pyramid partially solves this problem. However, the pyramid height isalso limited by the size of the downsampling/upsampling
filter [3].

w×h×N864 × 576 × 3
1227 × 818 × 3
1728 × 1152 × 3
864 × 576 × 71227 × 818 × 71728 × 1152 × 7

init. (s)
.75
1.5
3.0
1.5
3.0
6.0

update (s)
.82
1.6
3.2
1.5
3.1
6.0

total (s)
1.6
3.2
6.2
3.0
6.1
12.0

Table 1. Computation times for our technique. We computed results for 12 , 1 and 2
megapixel images. N is the number of images in the stack. The initialization builds
the Laplacian pyramids for each input image.
The update step computes the weight maps,
the corresponding Gaussian pyramids, andthe blending. For small image sizes (half to
one megapixel), the user gets interactive interactive feedback (about one second).

4.2. Performance
Our unoptimized software implementation performs exposure fusion in a matter of seconds; see table 1. Afterbuilding the Laplacian pyramids, our technique can provide
near-interactive feedback (see timings of update step). This
enables a user gain more control over the fusion process,as he or she can adjust the weighting of quality measures.
Additional controls on the input images, such as linear
and non-linear intensity remappings are also possible (likebrightness adjustment or gamma curves). This can be used
to give certain exposures more influence. Motivated by thework of Strengert et al. [27], we expect that our algorithm
could eventually run in real-time on graphics hardware.

4.3. Including Flash-Exposures
A flash exposure can fill in a lot of detail, but tends to
produce unappealing images, and it may include spurious
highlights and reflections. Recent work on flash photography has introduced techniques for combining flash/no-flash
image pairs [9, 22, 2]. Our technique can be used here as
well, as our quality measures are also relevant in this case.
Fig. 8 shows how our technique has successfully removed ahighlight and filled in details, similar to Agrawal et al. [2].
However, it cannot remove flash shadows [9] or unwanted
reflections [2].

4.4. Comparison of Quality Measures
Fig. 10 shows a comparison of our quality measures.
Exposure fusion is performed with either contrast, saturation or well-exposedness. The desk scene in the first row(a) Fused(b) Single exposure

Figure 6. A spurious low-frequency change
in brightness might occur due to the difference in exposure among the input images.
The result (a) appears too bright toward thebottom, which seems unnatural compared tothe input images. One of the input images isshown in (b) for reference.

comes out better with saturation turned on. Contrast makes
the background a bit dark, and well-exposedness darkens
the center of the monitor, making the result look unnatural.
For the house scene on the next row, saturation and wellexposedness produce vivid colors, which is less so for contrast. Finally, the last row shows how contrast retains details, which are not present in the saturation image (e.g. in
the water, and the buildings’ windows). Well-exposedness
yields an interesting image, but it looks less natural than theother two.
In general, we found that well-exposedness by itself produces fairly good images. However, in some cases it tends
to create an unnatural appearance, because it always favors
intensities around 0.5. Saturation and contrast does not havethis problem. But then again, the results from those measures are not always as interesting as those produced bywell-exposedness.

5. Conclusion
We proposed a technique for fusing a bracketed exposure
sequence into a high quality image, without converting to
HDR first. Skipping the physically-based HDR assembly
step simplifies the acquisition pipeline. It avoids camera
response curve calibration, it is computationally efficient,and allows for including flash images in the sequence.

(a) Durand et al. [8](b) Reinhard et al. [25](c) Li et al. [15](d) Our techniqueFigure 7. Comparison with several popular tone mapping techniques. Our algorithm yields image
quality that is competitive with the other results. See Fig. 9 for a more detailed inspection.

Our technique blends images in a multi-exposure sequence, guided by simple quality measures like saturationand contrast. This is done in a multiresolution fashion toaccount for the brightness variation in the sequence. Quality is comparable to existing tone mapping operators. Our
approach is controlled by only a few intuitive parameters,
which can be updated at near-interactive rates in our unoptimized implementation.
We would like to investigate different pyramidal image
decompositions, such as wavelets and steerable pyramids.
Also, we would like to include more measures, in particular one that would detect camera noise. An optimized GPU
implementation would enable the user to interactively control the fusion process, but could also be used to display amulti-exposure video stream [18] in real-time. Finally, we
would like to look into the applicability of our techniqueto other image fusion tasks, such as depth-of-field exten-

sion [19] and multimodal imaging [4].

Acknowledgements
Thanks to Jacques Joffre, Jesse Levinson, Min H. Kim
and Agrawal et al. [2] for sharing their photographs.
Part of the research at Expertise Centre for Digital Media(EDM) is funded by the European Regional Development
Fund.

References[1] A. Agarwala, M. Dontcheva, M. Agrawala, S. M. Drucker,
A. Colburn, B. Curless, D. Salesin, and M. F. Cohen.
Interactive digital photomontage. ACM Trans. Graph,23(3):294–302, 2004.

(a) Ambient(b) Flash(c) Fused

Figure 8. Combining a flash/no-flash image pair using our technique. Notice how the highlight isremoved, while detail and contrast has been transferred to the face. Images taken from Agrawal etal. [2].

(a) Durand & al. (b) Reinhard&al.

(c) Li & al.

(d) Ours

Figure 9. Comparison with several tone mapping techniques: closeups from Fig. 7. The
results produced by our algorithm exhibit
high contrast and good color reproduction.

[2] A. Agrawal, R. Raskar, S. K. Nayar, and Y. Li. Removing
photography artifacts using gradient projection and flashexposure sampling. ACM Trans. Graph., 24(3):828–835,
2005.
[3] P. Burt and T. Adelson. The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communication,
COM-31:532–540, 1983.
[4] P. J. Burt, K. Hanna, and R. J. Kolczynski. Enhanced image
capture through fusion. In Proceedings of the Workshop on
Augmented Visual Display Research, pages 207–224. NASA– Ames Research Center., Dec. 1993.
[5] P. E. Debevec and J. Malik. Recovering high dynamic range
radiance maps from photographs. In SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pages 369–378, New York,NY, USA, 1997. ACM Press/Addison-Wesley PublishingCo.
[6] J. DiCarlo and B. Wandell. Rendering high dynamic range
images,. In Proceedings of SPIE, volume 3965, Jan. 2000.

[7] F. Drago, K. Myszkowski, T. Annen, and N. Chiba.
Adaptive logarithmic mapping for displaying high contrast
scenes. Computer Graphics Forum, 22:419–426, 2003.
[8] F. Durand and J. Dorsey. Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph,21(3):257–266, 2002.
[9] E. Eisemann and F. Durand.
Flash photography enhancement via intrinsic relighting. In ACM Transactions
on Graphics (Proceedings of Siggraph Conference), volume 23. ACM Press, 2004.
[10] R. Fattal, D. Lischinski, and M. Werman. Gradient domainhigh dynamic range compression. ACM Transactions on
Graphics, 21(3):249–256, July 2002.
[11] A. Goshtasby. Fusion of multi-exposure images. Image andVision Computing, 23:611–618, 2005.
[12] M. Grundland, R. Vohra, G. P. Williams, and N. A. Dodgson. Cross dissolve without cross fade: Preserving contrast,
color and salience in image compositing. Computer Graphics Forum, 25(3):577–586, Sept. 2006.
[13] P. Haeberli. A multifocus method for controlling depthof field. http://www.graficaobscura.com/depth/index.html,
1994.
[14] G. W. Larson, H. E. Rushmeier, and C. D. Piatko. A visibility matching tone reproduction operator for high dynamic
range scenes. IEEE Trans. Vis. Comput. Graph, 3(4):291–
306, 1997.
[15] Y. Li, L. Sharan, and E. H. Adelson. Compressing and companding high dynamic range images with subband architectures. ACM Transactions on Graphics, 24(3):836–844, Aug.
2005.
[16] J. Malik and P. Perona. Preattentive texture discrimination
with early vision mechanism. Journal of the Optical Societyof America, 7(5):923–932, May 1990.
[17] S. Mann and R. Picard. Being ’undigital’ with digital cameras: Extending dynamic range by combining differently exposed pictures. In Proceedings of IS&T 46th annual conference., pages 422–428, May 1995.
[18] M. McGuire, W. Matusik, B. Chen, J. F. Hughes, H. Pfister, and S. Nayar. Optical splitting trees for high-precision

(a) Contrast(b) Saturation(c) Well-exposedness

Figure 10. Comparison of our quality measures. Exposure fusion is performed with each measureturned on separately. See Sec. 4.4 for a discussion. Bottom images courtesy of Jacques Joffre.

monocular imaging. Computer Graphics and Applications,
March 2007.
[19] J. M. Ogden, E. H. Adelson, J. R. Bergen, and P. J. Burt.
Pyramid-based computer graphics. RCA Engineer, 30(5),1985.
[20] S. N. Pattanaik, J. E. Tumblin, H. Yee, and D. P. Greenberg. Time-Dependent visual adaptation for realistic RealTime image display. In Proceedings of SIGGRAPH 2000,
Computer Graphics Proceedings, Annual Conference Series, pages 47–54, July 2000.
[21] P. Pérez, M. Gangnet, and A. Blake. Poisson image editing.
In SIGGRAPH ’03: ACM SIGGRAPH 2003 Papers, pages
313–318, New York, NY, USA, 2003. ACM Press.
[22] G. Petschnigg, R. Szeliski, M. Agrawala, M. F. Cohen,
H. Hoppe, and K. Toyama. Digital photography with flash
and no-flash image pairs. ACM Trans. Graph, 23(3):664–
672, 2004.
[23] R. Raskar, A. Ilie, and J. Yu. Image fusion for context enhancement and video surrealism. In Proceedings of the 3rd
Symposium on Non-Photorealistic Animation and Rendering, pages 85–152, 2004.

[24] E. Reinhard and K. Devlin. Dynamic range reduction inspired by photoreceptor physiology. IEEE Trans. Vis. Comput. Graph, 11(1):13–24, 2005.
[25] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda. Photographic tone reproduction for digital images. ACM Transactions on Graphics, 21(3):267–276, July 2002.
[26] E. Reinhard, G. Ward, S. Pattanaik, and P. Debevec. HighDynamic Range Imaging: Acquisition, Display and ImageBased Lighting. Morgan Kaufmann Publishers, Dec. 2005.
[27] M. Strengert, M. Kraus, and T. Ertl. Pyramid Methods in
GPU-Based Image Processing. In Workshop on Vision, Modelling, and Visualization VMV ’06, pages 169–176, 2006.
[28] A. Toet. Hierarchical image fusion. Mach. Vision Appl.,
3(1):1–11, 1990.
[29] J. Tumblin and H. E. Rushmeier. Tone reproduction for realistic images. IEEE Computer Graphics and Applications,13(6):42–48, Nov. 1993.
[30] G. Ward. Fast, robust image registration for compositing
high dynamic range photographcs from hand-held exposures. Journal of Graphics Tools: JGT, 8(2):17–30, 2003.