an adaptive tone mapping method for displaying high ... · tone mapping for hdr images 979 fig. 1....

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 26, 977-990 (2010)

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An Adaptive Tone Mapping Method for Displaying High Dynamic Range Images*

JINHUA WANG, DE XU, CONGYAN LANG AND BING LI

Institute of Computer Science and Engineering Beijing Jiaotong University

Beijing, 100044 China

Bilateral filter based tone mapping for rendering High Dynamic Range (HDR) im-

ages can not display all details in dark or highlight areas of an image. In order to solve the problem, we propose a Local Adaptive Bilateral Filter (LABF) method having three-fold improvement over the bilateral filter based method. First, since less correlation be-tween luminance and chrominance can produce better performance, we choose YUV color space with less correlation between luminance and chrominance, and only the lu-minance component Y is compressed. Second, to imitate the human response to light, we adjust the logarithmic base adaptively depending on each pixel's luminance value. This strategy can provide good contrast and detail preservation in dark areas while achieving the compression of high luminance range. Third, we propose an improved center/surround method to enhance visibility in the highlight or relatively darker areas further, which is a non-linear function with a weighted average of surrounding pixel values serv-ing a variable. The experimental results show that the proposed method LABF can obtain satisfactory results on various images with different scene contents. Keywords: tone mapping, local adaptive, bilateral filter, center/surround, high dynamic range

1. INTRODUCTION

High Dynamic Range (HDR) images typically cover a large range of luminance in-formation and are represented by more than 8-bits per channel. They are close to the range that perceived by human vision system. Moreover, HDR images stores linear values, implying that the value of one pixel is proportional to the value of light measured by a camera. In this sense, HDR images fully store the information of a scene. However, HDR images have a major inconvenience in application, that is, they can not be displayed cor-rectly on ordinary display devices such as printers or monitors. That is why tone mapping methods are designed, which scale the large range of luminance information that exists in the real word so that it can be displayed on devices those have much lower dynamic range.

Tone mapping methods can be broadly classified by spatial processing techniques into two categories: global and local methods [1]. For global methods, they usually com-press the dynamic range using gamma function, sigmoid function or histogram equaliza-tion. Each pixel is mapped based on global image characteristics regardless of its spatial location in a bright or dark area. The global methods are easier to implement and faster to perform. However, when the dynamic range of the scene is particularly high, these meth- Received September 23, 2008; revised December 4, 2008; accepted April 30, 2009. Communicated by Liang-Gee Chen. * This work was supported by National Nature Science Foundation of China (60803072) and Beijing Jiaotong

University Science Foundation (2007XM008), National High Technology Research of China (2007AA- 01Z168).


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ods tend to result in either graying out or losing visible details. For local method, differ-ent operations are applied to different pixels. In this case, one input value can produce more than one output values, which depends on the pixel value and the surrounding pixel’s values. The local methods can scale the image’s dynamic range to the output de-vice’s dynamic range while increasing the local contrast. However, they tend to produce ringing or haloing artifacts. Since the local methods are capable of compressing quite large dynamic range and also mimicking the local adaptation of the human visual system, more emphasis is recently put on developing this type of method. A brief of the local methods is given below.

Reinhard [2] proposed a tone mapping method for HDR rendering by simulating dodging and burning in traditional photography. iCAM06 [3] was an image appearance model that had been extended to render HDR images for displaying on ordinary devices. Meylan [4] proposed a center/surround retinex model for HDR rendering. In this model, the weights of surrounding pixels were computed with an adaptive filter method, which adjusted the shape of the filter to the high contrast edges in images. Meylan presented another tone mapping method [5] that was derived from a model of retinal processing. A method in gradient domain [6] was proposed by Fattal, it manipulated the gradient field of an image by attenuating the magnitudes of large gradients. Durand and Dorsey [7] used a bilateral filter to reduce the overall contrast while preserving local details in an image. The pixel’s luminance value is calculated using fixed logarithmic base. Notice that the method was performed calculation in the logarithmic domain, only because the difference of pixel values directly corresponds to contrast value. However, the perform-ance of different logarithmic bases is ignored. As a result, some details in dark or high-light areas could not be visible.

In this paper, we present a Local Adaptive Bilateral Filter (LABF) method, which has threefold improvement compared to [7]. First, YUV color space is chosen. Second, an adaptive logarithmic compression process is introduced, which adaptively varies loga-rithmic bases according to the pixel value. Third, an improved center/surround technique is applied, which is a non-linearity function with weighted average of surrounding pixel values serving a variable. Experimental results show that LABF can preserve more de-tails in dark or highlight areas.

The organization of the paper is as follows. The proposed method LABF is de-scribed in section 2. In section 3, the effectiveness of LABF is verified by experiments on images with different contents. Concluding remark is given in section 4.

2. LOCAL ADAPTIVE BILATERAL FILTER METHOD

In this section, the flowchart of LABF is demonstrated in Fig. 1. In summary, the procedure can be listed six steps as follows. Firstly, the RGB color space of an image (obtained by HDR radiance map) is converted into YUV color space, and only Y compo-nent is compressed. U and V components are stored for the final RGB image reconstruct- tion. Secondly, the local adaptive bilateral filter is applied to the Y components to achieve dynamic range compression. In this step, the base value for each pixel is stored. Thirdly, with the compressed Y obtained in the second step and the stored base values generates anew Y component. Fourth, an improved center/surround technique is applied to the new

TONE MAPPING FOR HDR IMAGES

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Fig. 1. Flowchart of LABF.

Y component for preserving more details in dark or highlight areas. Fifth, using the Y component produced by the former four steps and stored chrominance components U and V adjusts the chrominance values in the YUV color space. Sixth, the YUV image is converted back into output RGB image, which can be directly displayed on the ordinary devices. In the next subsections, we will describe each step in detail.

2.1 Color Space Choice

It is a well-accepted solution to treat luminance independently from chrominance, which can avoid artifacts and save processing time. There are many color spaces from which transforms can be chosen. The paper [8] confirms that less correlation between luminance and chrominance can produce better performance of the tone mapping meth-ods, and the images obtained by using YUV and PCA both have less hue shifts. However, when one scene contains few colors, using PCA would be lead to an ill-conditioned transformation matrix, which results in the failure of the whole method. So, in this paper, we choose the YUV color space. Notice that one of the key contributions in LABF com-pared with [7] is to use the YUV color space. In our paper, we make use of the variable I to denote the luminance component, which is similar to other typical tone mapping methods for describing the compression process, that is, I = Y.


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2.2 Local Adaptation Applied to Logarithmic Base

Bilateral filter is an edge-preserving filter, where the weight of each pixel is com-puted using a Gaussian function in the spatial domain multiplied by an influence func-tion in the intensity domain that decreases the weight of pixels with large intensity dif-ferences [7]. The influence function in LABF is another Gaussian function. The output of bilateral filter for a pixel p is given as:

1( ) ( ) ( ( ) ( ) ) ( )s r

bq

pF p G p q G I p I q I q

W σ σ∈Ω= − −∑ (1)

( ) ( ( ) ( ) ),s rp qW G p q G I p I qσ σ∈Ω= − −∑ (2)

where Gσs

denotes a Gaussian function with kernel σs in the spatial domain. Gσr repre-

sents a Gaussian function with kernel σr in the intensity domain. p denotes the treated pixel. q is one of pixels in the neighboring domain Ω. Wp is a normalization term. Fb(p) is the output of bilateral filter for pixel p. I(p) is the original luminance value for pixel p.

Inspired by [9], we introduce an adaptive method, which adjusts the logarithmic base according to each pixel’s luminance value. This is the second different point com-pared to paper [7]. In LABF, pixel’s luminance value is served as a variable to decide the base value, which can preserve more details and contrast in dark areas and compress luminance in highlight areas. The luminance values formed different bases in logarithmic domain are taken as input for bilateral filter. Base values for all pixels are stored for the final exponent calculation.

The base value for one pixel p is defined as:

min maxmax

( )( ) ( ) ,rI pB p B BI

= + × (3)

where parameter r is used to adjust the overall brightness of an image: small r value makes the mapped image relatively darker while large r value makes the image relatively lighter. The parameters Bmin and Bmax are used to decide the range of base values for the whole image. Imax denotes the maximum luminance value.

Then the value for pixel p in the logarithmic domain is given as:

Ia(p) = logB(p)(I(p)). (4)

The curves of logarithmic function with different bases are showed as in Fig. 2. From the figure, we can conclude that for pixels having low luminance value, the output luminance value could increase more when using small base; for pixels having high lu-minance, the output could compress more when using large base. That is to say, using small base for dark pixel, good contrast and visibility could be achieved while using large base for high pixel, compression could be obtained.

The decomposition of the luminance component of an image into base layer Ibase and detail layer Idetail is given as:


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Fig. 2. The curves of logarithm with different bases.

( ) ( ( )),bbase aI p F I p= (5)

( ) ( ) ( ).detail baseaI p I p I p= − (6)

We can see that for one pixel p the base layer Ibase(p) is the output of the bilateral filter, while the detail layer Idetail(p) is the difference between the input image and the base layer. In our paper, after the two-scale decomposition, only the base layer is compressed, the formula is defined as [7]:

Ic(p) = Ibase(p) × β + Idetail(p), (7)

where Ic(p) denotes the compressed luminance value for pixel p, the parameter β is used to achieve the base layer compression, it is image-dependent and an adaptive value. In our paper, we also define β = log(5)/(max(Ibase) − min(Ibase)) as [7].

Finally, the output value for pixel p of final exponential function is defined as:

Ibf(p) = B(p)Ic(p). (8)

2.3 Improved Center/Surround Method

The articulation of highlight areas or relatively darker areas from the above two processes is not visible enough to view. For example, the ground area of the image in Fig. 3 (b) is too bright to losing more details. In our paper, we propose an Improved Center/ Surround (ICS) method in order to solve the problem.

The proposed ICS shares similarity to the traditional center/surround methods, which uses the difference in the logarithmic domain between each pixel and a weighted average of the pixel values in its surround to serve as the treated pixel’s new luminance value I′(p). The weighted average is obtained by filtering an image with a low-pass Gaussian filter with kernel σg. The formula is given as follows:

I′(p) = log(I(p)) − log(I(p) ⊗ Gσg), (9)


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(a) Original image. (b) Image adjusted without ICS. (c) Image adjusted with ICS. Fig. 3. Comparison result related to ICS. The image courtesy of Laurence Meylan [4].

where ⊗ denotes the convolution operator. Common drawback of traditional center/surround method is that it tends to generate halo artifacts. The key difference of LABF compared to the traditional center/surround method is the weighted average usage. In LABF, we use the weighted average Ibf (p) ⊗ Gσk

for pixel p as a variable of the proposed function, where Ibf (p) is obtained in Eq. (8) and Gσk

is a Gaussian function with kernel σk. The weighted average value portrays the luminance environment in the neighboring do-main of a given pixel p. The formula of ICS is defined as follows:

( )( ) ( ),

( ) ( )k

k

mean bfnew bf

bf bf

I I p GI p I p

I p I p Gσ

σ

+ ⊗= ×

+ ⊗ (10)

where Inew(p) is the output of pixel p, Imean is the average value of the Ibf got from the former two steps. It is easily affected by extreme pixels when directly computed from total pixels of an image. We adopt a new computing Imean technique. Firstly, we sort the whole luminance values in ascending order, and then get the two end sides: the front 2% and after 98% of the image. Lastly, we calculate the average values of two side pixels, i.e., two values are got to taken as two new pixel values. For the remaining pixels and the new two values, we get the average value to serve as the mean value of the whole image.

Fig. 4 illustrates the role of weighted average value for one pixel p. It shows that given an image whose Imean = 0.45. If Ibf (p) < 0.45, the output luminance value could be increased. When the weighted average value is small (e.g. 0.1107), implying the treated pixel locates in relatively darker area. The output luminance increases more compared with the larger weighted average value (e.g. 0.9831). So using small weighted average can display more details in dark area. If Ibf (p) > 0.45, the output luminance value could be decreased. When the weighted average is relatively bright (e.g. 0.9831), the output luminance decreases less. Since small difference between weighted average value and treated pixel value can make the result more natural, ICS could reduce the luminance difference between the treated pixel and neighboring domain. It should be noted that for pixels around the mean value, the output is approximate the original value, that is to say, ICS can preserve the luminance values for pixels located mean value. We can see the result from the image in Fig. 3 (c), the visibility of highlight ground area processed using ICS is better than that without using.


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Fig. 4. The curves with different weighted averages.

Finally, the chrominance values for pixel p are obtained by using a scaling tech-nique, where the original values of U(p) and V(p) are multiplied respectively by the ratio of the treated luminance value Inew(p) to the initial luminance value Y(p). Notice that Inew(p) is output value for pixel p in Y component. The formula is defined as follows:

( ) ( )( ) ( ); ( ) ( ); ( ) ( ).

( ) ( )new new

new new new newI p I p

Y p I p U p U p V p V pY p Y p

= = × = × (11)

3. EXPERIMENTAL RESULTS

In this section, we firstly show the effectiveness of the proposed method LABF compared to typical tone mapping methods in HDR images compression. Then the ap-pearance enhancement of LABF compared with bilateral filter [7] for 8 bit images is demonstrated. Lastly, we propose a method to evaluate the capability of color appear-ance improvement to LABF.

The parameters in our experiment are set as follows. The kernel σs in the spatial domain is set to empirical value of 2% of the image size and σr = 0.35 as [7] did. Bmin = 2, Bmax = 10, r = 0.01 and σk = 20. These parameters are user-controlled according to the characteristic of images. However, they work well for all our test images, but in some situations where the dynamic range is quite high, one can vary these settings. At present, we use these empirical parameters, and how to decide their values adaptively according to the characteristic of input HDR image is my future work.

We compare LABF with method [7] and another adaptive method [12] firstly. The comparison results are shown in Fig. 5. The method [12] is an adaptive method based on spatial and information in the input image. We can see the images in Fig. 5 (a) treated by [7] are a little dark and some details can not be seen. Although the more detailed images in Fig. 5 (b) got by [12] can be visible, local contrast is lost compared to that of LABF. For example, the images in Fig. 5 (b) in the second row is a little washed out, and some chrominance information of leaves is lost. The images in Fig. 5 (c), more texture details of trunk are visible, the chrominance of leaves in both images is more natural to the per-ception of human eyes.


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(a) Bilateral filater [7]. (b) Adaptive method [12]. (c) LABF.

Fig. 5. Comparison results. The image courtesy of Paul Debevec.

(a) Original. (b) Bilateral filter [7]. (c) LABF.

Fig. 6. Comparison results. The image courtesy of Laurence Meylan [4].

In order to further demonstrate the performance of LABF in preserving details for displaying HDR images, we show another comparing result between LABF and [7] in Fig. 6. Due to the fixed base value over the whole image, bilateral filter [7] shows the weakness in preserving details in dark and light regions compared with the images treated by LABF. For example, some details of the trunk of tree and cloud in the sky of image in Fig. 6 (b) are lost compared to the image in Fig. 6 (c) obtained by LABF.

Fig. 7 compares our compressed result with the methods based on color appearance model [3], Retinex-based method [7] and retinal processing model [5]. As we can see more texture details of desk in the image in Figs. 7 (a) and (c) treated by [4, 5] respec-tively are lost, and the details of the pillar are lost more in the image Fig. 7 (b) treated by [3]. As a whole, more texture details of desk and the color of pillar in the image in Fig. 7 (d) treated by LABF are maintained, although some details of the color-checker’s bor-ders on the desk are lost.

In addition, LABF works also well in appearance enhancement for 8 bit images. We


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(a) L. Meylan [4]. (b) iCAM06 [3].

(c) Retinal-based [5]. (d) LABF.

Fig. 7. Comparison results. Image courtesy of Meylan [4].


Fig. 8. Comparison for 8 bit image. Image courtesy of [10].


Fig. 9. Comparison result for 8 bit image. Image courtesy of [10].


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(a) Original images. (b) Gradient domain [6]. (c) LABF.

Fig. 10. Comparison results. The Images courtesy of [6].

can see that more details of jersey in the image in Fig. 8 (c) can be seen compared to image in Fig. 8 (b). We can see that the cover of book in the median part in image Fig. 9 (b) is brighter than that of in the image in Fig. 9 (c). However, the left part in the image Fig. 9 (b) is a little dark, which results in some details are lost. We can see that LABF can produce better appearance in preserving details and contrast.

Fig. 10 shows the comparing results between LABF and gradient-based method [6]. The images in Fig. 10 (a) are original images, images in Fig. 10 (b) are treated using [6], and images in Fig. 10 (c) are the results of LABF. As to the details preservation, it is difficult to say which one is better between the results of the methods [6] and LABF. However, the main problem induced by [6] is that it brings out the contrast reversal phenomenon. For example, in the first row, the brightness in the left-top sky of the image in Fig. 10 (b) is darker than the leaves in the right-bottom foreground, which are in reverse in the original image in Fig. 10 (a). We can see that the images in Fig. 10 (c) treated by LABF can pre-serve more details in the areas of sky and building, while maintaining better contrasts.

We propose a technique to validate the performance of tone mapping methods for 8 bit image owing to the following two reasons: (1) there has been no standard objective evaluation method universally available for measuring the quality of displayed HDR images until now. Most existing evaluating methods are only based on a series of subjec-tive experiments; (2) the goal of tone mapping is to obtain the same perception of the eye when sees the mapped image on a display device as the real scene does. Unfortunately, in practice, the original scenes are usually unknown.

We use the data set [10], which consists of 30 scenes. Each scene was imaged under 11 illuminants. Moreover, for each image the color of illuminant is measured. In LABF, we use the measured illuminant to obtain images that can simulate the original scenes. In


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our experiment, we choose 30 images under the condition that not only each image be-longs to different scenes, but also all the images are of the same “ph-ulm” illumination (one of the 11 illuminants). The 30 scenes can be seen in Fig. 3 of [10]. We make use of the measured illuminant for adjusting the input image to a standard image Is under ca-nonical illuminant, which serves as reference for evaluating the performance of LABF. It should be noted that Is is only the simulation of real world in our method, and serving it as standard can not demonstrate LABF can achieve the ability of color constancy. For getting the standard image, the diagonal model [10] has to be used, which maps the image taken under one illuminant to the image taken under another illuminant, by simply scal-ing each channel independently. For concreteness, consider a scene with a grey patch. Suppose that the color of the unknown illuminant is σU = (σ1

U, σ2U, σ3

U), and the color of the known canonical illuminant is σC = (σ1

C, σ2C, σ3

C). Then the grey patch can be mapped from the unknown case to the canonical case by scaling the ith channel using σi

C/σiU. We

define the measured illuminant as Rc, Gc and Bc, then the scaling coefficient of the three channels is defined as (255/Rc, 255/Gc, 255/Bc)T.

We define the Chrominance Correlation Measure (CCM) as the mean of the corre-lation coefficients between the chrominance channels It

R, ItG and It

B in the treated image and the chrominance channels Is

R, IsG and Is

B in the standard image respectively. The for-mula of CCM is defined as:

( , ) ( , ) ( , )3

R R G G B Bt s t s t scorr I I corr I I corr I I

CCM+ +

= (12)

( , )( , )

( , ) ( , )

i ii i t st s i i i i

t t s s

cov I Icorr I I

cov I I cov I I= (13)

1

1( , ) ( ( ) )( ( ) )N

i i i i i it s t t s s

pcov I I I p I I p I

N

− −

=

= − −∑ (14)

where N is the number of pixels in the image. It represents the treated image. Is denotes the standard image. i = R, G or B. The comparing results of LABF compared to methods [3, 5, 7] are illustrated in Fig. 11. The value of Y-coordinate is the correlation value of image. As we known, the larger is the correlation, the closer is the similarity between the two images. We can draw a conclusion from the Fig. 11 that the overall correlation val-ues of LABF are larger than other methods, and the correlation values’ change range of LABF is smaller than others. These arguments can prove the performance of LABF in preserving the chrominance information related to the real scenes.

In order to further validate the effectiveness of LABF compared to [7], we calculate the values of entropy and mutual information (MI) for the results in Figs. 8 and 9. We know that entropy can be used to measure the overall information in the result image, that is, the capability of details preservation for tone mapping methods. Moreover, it is well known that MI is a basic concept of information theory measuring the statistical depend-ence between two random variables and the amount of information that one variable contains about the others. In our paper, we use MI to measure the amount of information that


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Fig. 11. The comparing correlation results of LABF compared with typical tone mapping methods.

Table 1. Evaluation between LABF and bilateral filter [7]. MI Entropy CCM LABF Bilateral filter [7] LABF Bilateral filter [7] LABF Bilateral filter [7]

Fig. 8 3.1395 2.9907 4.5814 2.0880 0.9949 0.9784 Fig. 9 3.5733 3.4245 5.7389 5.5206 0.9930 0.9887

treated image It contains the standard image Is. As to MI, more details can be found in [13]. Notice that for both criteria, the larger the value, the better is the result. The com-parison result is illustrated in Table 1. We can see that the results obtained by LABF for Entropy and MI are both larger than those of [7], which are consonant with the results obtained by CCM.

4. CONCLUSION

In this paper, a Local Adaptive Bilateral Filter (LABF) method performing in the YUV color space is proposed. In the paper [11], J. Kuang shows that the bilateral filter significantly and consistently outperforms other tested methods for both preference and accuracy. We put forward three techniques to improve the method [7], although the proc-essing time is a little higher than [7]. The experimental results show that LABF can ob-tain comparative or even a little better result compared to typical tone mapping methods.

REFERENCES

1. E. Reinhard, G. Ward, S. Pattanaik, and P. Debevec, High Dynamic Range Imaging- Acquisition, Display and Image-based Lighting, Morgan Kaufman Publishers, San Francisco, 2006.

2. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” ACM Transactions on Graphics, Vol. 21, 2002, pp. 267-276.

3. J. Kuang, G. M. Johnson, and M. D. Fairchild, “iCAM06: A refined image appear-


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ance model for HDR image rendering,” Journal of Visual Communication and Im-age Representation, Vol. 18, 2007, pp. 406-414.

4. L. Meylan and S. Süsstrunk, “High dynamic range image rendering using a retinex- based adaptive filter,” IEEE Transactions on Image Processing, Vol. 15, 2006, pp. 2820-2830.

5. L. Meylan, D. Alleysson, and S. Süsstrunk, “A model of retinal local adaptation for the tone mapping of color filter array images,” Journal of the Optical Society of America A, Vol. 24, 2007, pp. 2807-2816.

6. R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” ACM Transactions on Graphics, Vol. 21, 2002, pp. 249-256.

7. F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic- range image,” ACM Transactions on Graphics, Vol. 21, 2002, pp. 257-266.

8. L. Meylan and S. Süsstrunk, “The influence of luminance on local tone mapping,” in Proceedings of the 13th Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, 2005, pp. 276-282.

9. F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic mapping for displaying high contrast scenes,” in Proceedings of the 24th Annual Conference on European Association for Computer Graphics, Vol. 22, 2003, pp. 419-426.

10. K. Barnard, L. Martin, A. Coath, and B. Funt, “A comparison of computational color constancy algorithms, Part II: Experiments with image data,” IEEE Transactions on Image Processing, Vol. 11, 2002, pp. 985-996.

11. J. Kuang, H. Yamaguchi, C. Liu, G. M. Johnson, and M. D. Fairchild, “Evaluating HDR rendering algorithms,” ACM Transactions on Applied Perception, Vol. 4, 2007, pp. 1-27.

12. X. G. Li, K. M. Lam, and L. Shen, “An adaptive algorithm for the display of high- dynamic range images,” Journal of Visual Communication and Image Representa-tion, Vol. 18, 2007, pp. 397-405.

13. G. Qu, D. Zhang, and P. Yan, “Information measure for performance of image fu-sion,” Electronics Letters, Vol. 38, 2001, pp. 313-315.

Jinhua Wang (王金华) received the M.E. in Computer Science from Changchun University of Technology in 2006. He is pursuing the Ph.D. degree in Institute of Computer Science and Engineering, Beijing Jiaotong University, Beijing, China. Her current research interests include high dynamic range images and visual perception theory.


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De Xu (須德) received the M.E. in Computer Science from Beijing Jiaotong University in 1982. He is now a Professor in Institute of Computer Science and Engineering, Beijing Jiaotong University, Beijing, China. He has published more than 100 pa-pers in international conferences and journals. His research inter-est includes database system, computer vision and multimedia processing.

Congyan Lang (郎丛妍) received the Ph.D. degree in In-stitute of Computer Science and Engineering, Beijing Jiaotong University in 2006, Beijing, China. Her current research interests include image processing, visual perception.

Bing Li (李兵) received the B.E. in Computer Science from Beijing Jiaotong University in 2004. He is pursuing the Ph.D. degree in Institute of Computer Science and Engineering, Beijing Jiaotong University, Beijing, China. His current research interests include color constancy, visual perception and computer vision.

an adaptive tone mapping method for displaying high ... · tone mapping for hdr images 979 fig. 1....

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