Adaptive extended piecewise histogram equalisation for dark image enhancement

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1 IET Image Processing Research Article Adaptive extended piecewise histogram equalisation for dark image enhancement ISSN Received on 4th April 2014 Revised on 19th May 2015 Accepted on 29th May 2015 doi: /iet-ipr Zhigang Ling 1, Yan Liang 2, Yaonan Wang 1, He Shen 3, Xiao Lu 1 1 College of Electrical and Information Engineering, Hunan University, Changsha , People s Republic of China 2 School of Automation, Northwestern Polytechnical University, Xi an , People s Republic of China 3 Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando 32817, USA zgling_hunan@126.com Abstract: Histogram equalisation has been widely used for image enhancement because of its simple implementation and satisfactory performance. However, traditional histogram equalisation uniformly redistributes an entire histogram or multiple piecewise histograms with the same equalisation strategy, which may produce unnatural artefacts, overenhancement or under-enhancement in wide dynamic range dark image enhancement. This study proposes an adaptive extended piecewise histogram equalisation algorithm AEPHE) for dark image enhancement. First, an original histogram is divided into a group of extended piecewise histograms. Then, an adaptive histogram equalisation, which balances intensity preservation and contrast boosting, is further developed and respectively applied to these extended piecewise histograms. The final histogram for image enhancement is produced by a weighted fusion of these equalised histograms. The experimental results indicate that AEPHE is superior to multiple state-of-the-art algorithms. 1 Introduction Image enhancement aims to improve the visual appearance of input images and provide better transformed representations through analysis, detection and recognition for higher level image or video processing, and it has been actively discussed in the fields of image processing and computer vision for the past several decades [1 9]. However, it remains difficult to design an appropriate image enhancement method which works universally for various applications, for example, wide dynamic range images present additional challenges. As it is easy to implement and produce satisfactory results in many cases, histogram equalisation HE) has become one of the most popular image enhancement methods. HE uniformly redistributes high dynamic range greyscale to increase the average difference between any two altered grey levels. For example, global HE GHE) [10] based on a cumulative distribution function redistributes an original histogram for contrast enhancement. However, significant peaks in original histograms usually trigger excessive enhancement and unnatural appearance. Some adaptive HEs have been developed to avoid this problem, including adaptive local HE ALHE) [11] and contrast-limited adaptive HE CLAHE) [12]. Both ALHE and CLAHE split the input image into numerous small windows, and then apply HE to each window for contrast enhancement. However, lack of global histogram information sometimes leads to over-enhancement or undesirable checkerboard effects. To adjust contrast enhancement, a histogram modification framework HMF) [13] formulates contrast enhancement as an optimisation problem. Furthermore, a two-dimensional HE 2DHE) [14] boosts image contrast by increasing grey-level differences between the neighbouring pixels. On the basis of 2DHE, a layered difference representation of 2D histograms is present to enhance images [15]. Adaptively modified histogram equalisation AMHE) [16] modifies the probability density function PDF) of the greyscale and then applies histogram specification to the modified PDF. However, over-enhancement and under-enhancement still can occur and some artefacts appear in some smooth regions because these methods entirely redistribute the original histogram. As a result, improved HE methods have been achieved with multiple piecewise histograms, including brightness preserving bi-he BBHE) [17], equal area dualistic sub-image HE [18], minimum mean brightness error bi-he MMBEBHE) [19], recursive mean-separate HE RMSHE) [20] and brightness preserving dynamic fuzzy HE BPDFHE) [21]. The BBHE splits the original histogram into two piecewise histograms and then independently equalises them in the fixed grey range for improved brightness preservation. An extension of BBHE, the MMBEBHE [19] separates the original histogram using some threshold levels in pursuit of a minimum absolute mean brightness error AMBE). The RMSHE [20] recursively divides the original histogram into several piecewise histograms via local mean values, and respectively equalises them. The BPDFHE [21] adopts fuzzy statistics of an input image to improve grey-level brightness and preserve contrast, and Celik et al. [22] employed the Gaussian mixture model to partition the original histogram. These equalisation methods provide better brightness preservation. However, because of adopting the same strategy for each piecewise histogram, these equalisation methods fail to avoid either unexpected over-enhancement with artefacts in bright regions or under-enhancement in dark regions of images, particularly in wide dynamic range images. To the best of our knowledge, this issue is important but still open. In this paper, we propose an adaptive extended piecewise HE algorithm AEPHE) for the enhancement of dark images with a wide dynamic range. First, we propose two novel measures, one for intensity preservation measure and one for contrast boosting, to represent the statistical characteristics of the original histogram. To balance intensity preservation and contrast adaptively, we further develop a novel adaptive HE based on these two measures to avoid unexpected over-enhancement or under-enhancement. In addition, we present an extended piecewise histogram partition strategy to improve dark region boosting. Finally, all equalised piecewise histograms are fused by a weighting function in order to smoothly merge the effect in the overlapping parts. The experimental results demonstrate that AEPHE significantly enhances dark regions without introducing excessive enhancement or unnatural artefacts. The paper is organised as follows. Section 2 briefly introduces the related HMF. Section 3 first develops the intensity preservation measure and the contrast boosting measure, and then presents the 1012 & The Institution of Engineering and Technology 2015

2 implementation of AEPHE in detail. Section 4 provides experimental results and analyses and Section 5 draws a conclusion. 2 Related works Arici et al. [13] presented a HMF to exploit the available dynamic range for various contrast enhancements. According to the maximum entropy principle, an equalised or target histogram H t is expected to be a uniformly distributed histogram H u. Concurrently, the distance between H t and the original histogram H x should be minimised for the minimum mean square error. In relation to smooth mapping, the target histogram should have few spikes and no abrupt changes, that is, H t should have the minimum amount of deviations between its components or grey levels. Thus, the HMF formulates image enhancement as an optimisation problem with the consideration to the smoothness of the target histogram, the difference between H t and H x, and the distance between H t and H u. The target histogram can be solved by the following equation: H t = arg min a H H x 2 2 +b H H u ) 2 +g DH ) H where denotes Euclidean norm, and the regularisation parameters α, b and g are the weighting factors of multiple performance indexes and { } subjected to the normalised constraint: a + b + g = 1, a, b [ [ 0, 1], g [ [ 0,1) and D is a bidiagonal difference matrix and described as follows: D = The solution of 1) is given by [8] H t = a + b)u + gd T ) 1aH D x +bh u ) 3) where U is an identity matrix. The parameter a controls the contribution from the original histogram. The larger value of a reflects more significant contribution from the original histogram H x, and the enhanced image will include more statistical characteristics of the input image. Similarly, the higher value of b emphasises more contribution from the uniformly distributed histogram H u, and thus the resultant image will have the higher 2) contrast. The parameter g contributes to the smoothness of the equalised histogram, but it does not significantly produce grey change in the enhanced image. Therefore, it is often set as a fixed value. Unfortunately, these parameters a and b are often hand picked, and cannot be automatically selected based on the grey properties of input images. Moreover, the HMF entirely equalises the original histogram, instead of the piecewise histogram, which often produces excessive enhancement, under-enhancement or unnatural looking artefacts in enhanced images with wide dynamic range. As a result, this study seeks an adaptive HE for dark image enhancement with the designed regularisation parameters. 3 Proposed AEPHE algorithm Fig. 1 summarises the process of AEPHE. First, the input image is transformed to hue, saturation, value HSV) colour space and the luminance component V) is split out. Then, by using EPHE strategy, the original histogram of luminance component is split into multiple piecewise histograms which are then extended to obtain three extended piecewise histograms. Next, based on two proposed intensity preservation and contrast boosting measures, an adaptive HE, which balances the intensity preservation and contrast boosting, is developed and respectively applied to these extended piecewise histograms. Finally, a weighting function is adopted to integrate these equalised histograms and to obtain the comprehensive equalised histogram for the luminance component, which is used for dark image enhancement. 3.1 Intensity preservation measure and contrast boosting measure Generally speaking, dark regions in an image should be significantly boosted to improve the visual quality, while bright regions should be less boosted or even compressed to avoid over-enhancement. Hence, an intensity preservation measure, which determines the intensity boosting level for an input histogram, can be defined as follows: M I = max ) 1 L L i=1 n n i=1 iwi), MI l i where n i is the pixel number with the grey value i in the original ) ) 2/2s histogram of the input image, wi) = exp i I 2 max ) measures the intensity distance between the grey level i and the maximum grey value I max, I max is the upper bound of the grey value and set at 255 in this paper, s is suggested to be 0.2I max. 4) Fig. 1 Schematic diagram of the proposed AEPHE algorithm & The Institution of Engineering and Technology

3 L is the grey-level number and M l I is a lower bound of M I. It is easy to verify that the M I will be large if an input image or region has many high-intensity pixels, which means that the corresponding intensity need to be slightly boosted. Otherwise, the M I will be small and the input image or region needs to be greatly boosted. However, whether an image or region needs to be enhanced or not, is not only determined by its intensity information. For a dark region with low contrast, or flat area, it should be slightly enhanced or even not be boosted, while a dark region with a middle or high contrast should be significantly enhanced. In image processing, the gradient magnitude is often used to determine the contrast. The areas with slight gradient magnitudes are generally flat with low contrast, while the areas with sharp gradient magnitudes have high image contrasts. However, these regions with varying intensities may share the same gradient magnitude, because the gradient magnitude does not solely depend on the flatness of the region but also relies on its intensity. Hence, the local gradient magnitude is often normalised to accurately identify image contrast. Thereby, a contrast boosting measure is proposed to determine the level of contrast enhancement as follows: 1 M c = min L i=1 n i L i=1 G n x j ), Mc h 5) x j [Ri) where Ri) is the set of pixels with the grey level i in the original histogram and n i is the amount of pixels in the Ri). G n x j ) is the normalised local variance of the pixel x j in the input image and represented by G n x j ) = Gx j )/Ix j ), and Gx j ) is the gradient magnitude of the pixel x j and is computed by G = G 2 x + G2 y. Here, G x = I S and G y = I S T, denotes the convolution operation. S is a Sobel operator template and Mc h is an upper bound of M c. It can be seen that M c determines how much an original image should be boosted. Generally, an input image with a higher normalised contrast often has a higher M c, and thus it needs more enhancement so that the enhanced image will achieve the higher contrast and vice versa. From 4) and 5), it can be seen that the intensity preservation and contrast boosting measure have the opposite effects and they may determine image enhancement together. If an input image has the low intensity and high normalised contrast distribution, it is more likely to be greatly enhanced as M I is relatively small and M c is relatively large. On the contrary, if an input image has a high-intensity distribution and the flat areas, M I will become relatively large and M c will become small, it will be slightly enhanced. Moreover, while an input image has large values of M I and M c, or small values of M I and M c, the moderate enhancement is needed. 3.2 Extended piecewise histogram separation EPHS) strategy To avoid under-enhancement for the dark images, this paper proposes an EPHS to isolate the original histogram before the application of HE. Similar to the BBHE and MMBEBHE, the first step of the EPHS strategy is to equally divide the original histogram into N non-overlapping piecewise histograms h k k [ [1,..., N]), since the partition method based on the Gaussian mixture model has a high computational complexity [22]. The next step for both the BBHE and MMBEBHE is to individually equalise these piecewise histograms using the same strategy, and to limit the grey output range of these redistributed piecewise histograms. Conversely, the EPHS strategy expands each piecewise histogram h k by filling the outside grey range of h k with zeros to create an extended piecewise histogram H x ) k k [ [1,..., N], which remains the same length as the original histogram, and H x k is thus described as follows: H x k = [ 0,..., h k,..., 0 ] 6) Next, this study applies the adaptive HE developed in Section 3.3 to these extended piecewise histograms. The EPHS strategy permits the intensity distribution in each piecewise histogram to be redistributed to a wide dynamic grey range through HE, which beneficially boosts intensity values in dark regions. 3.3 Adaptive HE After the application of EPHS, these extended piecewise histograms have different intensity and contrast distributions, thus different HE strategies are needed to suppress unexpected enhancement and boost the dark regions. According to the definition in Section 3.1, the intensity preservation measure and contrast boosting measure have the similar meaning to the parameters α and β described in 3), respectively. While the original histogram has many low-intensity pixels, the M I will be small and the parameter α should be small so that the less contribution comes from the original histogram and the corresponding image will be sufficiently improved. Otherwise, the parameter α should keep a large value as the M I so that the high-intensity pixels will be slightly enhanced. Similarly, while the input image has a high normalised contrast, the parameter b will be a large value as the M c so that the equalised histogram more closely approaches to the uniform distribution, and this image will be better enhanced and vice versa. Therefore, this paper introduces the intensity preservation measure, and contrast boosting measure to define α and b for each extended piecewise histogram as follows: M I a = 7) M I + M c M c b = 8) M I + M c The regularisation parameter g described in 3) is set as zeros because it does not substantially influence the enhanced image. Once the adaptive regularisation parameters, the expected uniformly distributed histogram H u and extended piecewise histogram H x k are determined, an adaptive HE is applied to determine the equalised histogram H t k via 3). However, as shown in 6), the H x k has a limited grey range, and it will be redistributed to the full range from 0 to 255 if adaptive HE is directly applied to H x k, which may produce over-enhancement. To deal with this problem, H x k should be redistributed into a uniform distribution in a limited grey-level range, not over the full level range. In other words, the uniformly distributed histogram H u described in 3) cannot be defined as one during the full range of [0, 255]. Thus, the uniformly distributed histogram H u k for each H x k is confined to a limited range and reassigned as follows: { H u k i) = W k i), i Ó h k 1, i [ h k where the weighting function W k i) is given by W k i) = exp i u k) 2 ) 2s 2 k 9) 10) where u k and s k are the mean and standard deviation of the weighting function W k. To ensure the same redistribution in both symmetrical directions of H x k, u k is set as the middle point of h k, that is, u k = h 1 k + ) he k /2, where h 1 k, h e k is the first and the last grey level in the piecewise histogram h k, respectively. The standard deviation s k denotes the influence sphere of the H u k and s k = max s k th, wk d /2 ), where w k d = h e k h1 k is the covering width of h k. As shown in Fig. 2, s k th is a threshold for suppressing over-enhancement and is suggested as 128 Iu k in this paper, and Iu k is the mean intensity value derived from the corresponding piecewise histogram h k & The Institution of Engineering and Technology 2015

4 N is set as 3 in this paper. The parameters M l I and M h c 0.05 and 1, respectively. 4.1 Qualitative measures are set as Fig. 2 Weight functions for H x k with N = 3 for image Advertisement bins are multiplied by 4 for better display) 3.4 Integration of multiple equalised histograms After the application of the adaptive HE, each equalised histogram has the same length as the original histogram. The final equalised histogram is the weighted mixture of all the equalised extended piecewise histograms and defined as follows [4]: H t i) = N k=1 w k i)h t k i) 11) where w k i) is the normalised weighting function for the grey level i in the kthequalised histogram H t k, and defined as w k i) = W k i)/ N j=1 W ji) and the weighting function W k i) is defined in 10). The output image will be easily produced through histogram modification after the final equalised histogram is obtained. Fig. 3 summarises the implementation of AEPHE. 4 Experimental results To comprehensively test AEPHE, this paper first qualitatively evaluates the performance of AEPHE on various images, and further analyses the impacts of the parameter N and different HE strategies. Next, we quantitatively compare AEPHE with some state-of-the-art algorithms. To reduce the burden of computation, Image enhancement results: Fireworks, the input image in Fig. 4a, shows fireworks sparkling in the dark sky against a backdrop of distant city lights, where the details under the skyline are unclear. Fig. 4b shows the enhanced result of AEPHE and Fig. 4c gives the corresponding grey-level mapping functions of the input and output grey levels for different algorithms. As shown in Fig. 4c, the MMBEBHE, AMHE and brightness preserving dynamic fuzzy histogram equalisation BPDFHE) slightly improve the luminance of the dark sea surface, and 2DHE, MMBEBHE and RMSHE enhance the bright city regions more than the BPDFHE and AMHE. The GHE and HMF significantly improve the average brightness of the dark regions. However, the GHE also boosts noise and produces excessive enhancement in the dazzling fireworks regions. By comparison, as shown in Fig. 4b, the AEPHE increases image contrast of the dark regions and produces an enhanced image without degradations and over-enhancement in the sky and with clear details of the city. Fig. 5a shows the input image Sunset, which depicts a village in a dark region against a bright sky background. The bright sky causes the rest of the input image, that is, the village region, to appear dark with obscured details. The GHE, MMBEBHE, 2DHE and HMF not only do not improve the luminance of the dark region but also produce severe degradation in some bright areas of the sky, because the same maximum grey level is misclassified into different grey levels, as shown in Fig. 5c. The RMSHE separately equalises the low and high grey levels of the original histogram to obtain distinct intensity distributions, which boost the brightness of the village regions. Unfortunately, an incorrect grey-level mapping produces some artefacts in the sky. Although the BPDFHE performs well compared with the MMBEBHE and 2DHE in regards to degradation, it does not sufficiently boost the dark regions. The AMHE does not produce any degradation, but it darkens the bright areas of the sky and fails to boost the brightness of the dark regions. Conversely, because of adopting different equalisation strategies, the AEPHE simultaneously enhances the low, middle and high grey levels and provides an output image with visible details in the village region and without image degradation in the sky. Similar experiments were carried out on different dark images described in [4], including images Advertisement, Street, Dark ocean and Fountain. Compared with all of these methods, the AEPHE produces similar enhancement results to 2DHE, and considerably increases the intensity of dark regions while maintaining the intensity of bright region. According to these grey-level mapping functions, we can see that the AEPHE not only significantly Fig. 3 Algorithm of AEPHE & The Institution of Engineering and Technology

5 Fig. 4 Enhancement results for image Fireworks a Input image Fireworks b Our enhanced result c Grey-level mapping functions using different methods Fig. 5 Enhancement results for image Sunset a Input image Fireworks b Our enhanced result c Grey-level mapping functions using different methods enhances the dark regions of the input images, but also produces no excessive enhancements or degradations in the bright regions Performance analysis on parameter N and different HE strategies: The AEPHE equally divides the original histogram into N non-overlapping piecewise histograms, and the parameter N may affect the enhanced results. Figs. 6a and b present grey-level mapping functions using different N values for the images Sunset and Dark ocean. When N is set as 1, the AEPHE directly equalises the original histogram, but it cannot efficiently enhance the dark regions of Sunset because the pixels in the dark regions only account for a small portion of the total pixels. Although the AEPHE sufficiently enhances the dark regions of Dark ocean, it simultaneously over-enhances the bright regions in the input image. While the original histogram is divided into two piecewise histograms, the AEPHE can sufficiently enhance the dark regions, but it still brings over-enhancement for the bright regions of the image Dark ocean. When N is larger than 2, these AEPHEs with different N values produce the similar results wherein the dark regions are effectively enhanced and bright regions are not over-enhanced because the standard deviation in the weighting function W k i) is mainly controlled by the threshold s k th. Moreover, the increase of the value N will raise the computational burden of the AEPHE. Therefore, this paper sets the parameter N at 3. To further evaluate the performance of adaptive HE and EPHS strategies for dark image enhancement, four equalisation strategies, fix-ranged piecewise histogram equalisation FPHE), adaptive fixranged piecewise HE AFPHE), EPHE and AEPHE, are applied to the above-mentioned images. FPHE fixes the grey output range of each piecewise histogram and directly applies a standard image equalisation algorithm to them. AFPHE applies an adaptive HE strategy to each piecewise histogram without the modification of the uniformly distributed histogram. The EPHE uses EPHS to divide the original histogram, and independently adopts a standard image equalisation algorithm to each extended piecewise histogram before obtaining the final equalised histogram. Figs. 6c and d provide grey-level mapping functions based on four different equalisation strategies for Sunset and Dark ocean. Fig. 6c shows that the FPHE and EPHE both misclassify the same maximum grey level into different grey levels, leading to the production of degradation and unnatural images. Conversely, as a result of the adaptive HE strategy, the AEPHE and AFPHE can discriminate different grey levels and produce satisfactory output images. The image Dark ocean has several dark regions, but, as shown in Fig. 6d, the AEPHE and EPHE boost the dark regions more significantly than FPHE and AFPHE, and produce no over-enhancement or artefacts in the bright regions because of adopting the EPHS strategy. However, it must be pointed out that the non-uniform and other partition strategies can also be used in EPHS, but their impacts on the enhanced image will be determined in the future. 4.2 Quantitative measures To quantify these HE algorithms, this study utilises three quantitative evaluation metrics defined by Celik [14], namely normalised discrete entropy, discrete entropy and contrast measure, and AMBE to evaluate the performance of image enhancement method. The normalised discrete entropy is used to measure the relative discrete entropy between the input image X and the output image Y, and is defined as follows: 1 DE n X, Y) = ) )) 12) 1 + log K) DEY) / log K) DEX) 1016 & The Institution of Engineering and Technology 2015

6 Fig. 6 Grey-level mapping function map based on different N and HE strategies a Grey-level mapping function map based on different N for image Sunset b Grey-level mapping function map based on different N for image Dark ocean c Grey-level mapping function based on different HE strategies for images Sunset d Grey-level mapping function based on different HE strategies for images Dark ocean where DEX) and DEY) are the discrete entropy of the image X and the image Y, respectively, with K representing the distinct grey levels and log K) representing the maximum entropy value. For DE n X, Y). 0.5, the higher the discrete entropy of the output image, the wider dynamic range the image enhancement method provides. Discrete entropy and contrast measure DECM) is described as i, j) and a size of 3 3 in the input image X, and H and W are the height and width of the input image, respectively. If the output image Y has a weaker contrast than the input image X, then CM n X, Y), 0.5 and otherwise, CM n X, Y) Furthermore, brightness preservation may be regarded as a pre-existing condition in image enhancement. In this paper, AMBE [19] is adopted and defined as follows: 2 DECMX, Y) = )) )) 13) 1/ DE n X, Y) + 1/ CMn X, Y) 1 AMBEX, Y) = 1 + MBX) MBY) 16) where DECMX, Y) [ [0, 1] and it provides the highest value when the normalised DECM are high, and produces the lowest value when the normalised DECM are low. CM n X, Y) is the normalised contrast measure between the input image X and the output image Y, and is defined as follows: 1 CMX) CM n X, Y) = 2 CMX) CMY) 14) where CMX) is the average contrast value for the image X and is computed by CMX) = 1 H W HW i=1 j=1 Xi, j) E x i, j) 15) here E x i, j) is the average value in the local window with a centre of where MBX) and MBY) are the average values of the images X and Y, respectively. A higher AMBE corresponds with a better brightness preservation. Tables 1 3 show the comparison of these three metrics. The DE n values in Table 1 show that AEPHE performs better than most of the existing methods. The GHE would be expected to yield a higher entropy value as it creates a more uniform histogram distribution. However, the bins grouping in GHE may decrease the DE n value. The BPDFHE splits the original histogram into two sub-histograms and then independently equalises the two sub-histograms, so it yields a higher entropy value than the GHE. The AMHE and 2DHE also produce a comparable DE n value to the HMF. Although the AEPHE gives a lower entropy value than the 2DHE and AMHE for some images, it outperforms the 2DHE and AMHE in the average DE n and significantly improves the visual quality of the input image. Most importantly, the AEPHE does not create any degradation in the output image. & The Institution of Engineering and Technology

7 Table 1 Quantitative comparison in DE n The bold values express the best metric values) Image GHE MMBEBHE RMSHE BPDFHE AMHE 2DHE HMF AEPHE Sunset Fireworks Advertisement Dark ocean Street Fountain Average Table 2 Quantitative comparison in DECM The bold values express the best metric values) Image GHE MMBEBHE RMSHE BPDFHE AMHE 2DHE HMF AEPHE Sunset Fireworks Advertisement Dark ocean Street Fountain Average Table 3 Quantitative comparison in AMBE The bold values express the best metric values) Image GHE MMBEBHE RMSHE BPDFHE AMHE 2DHE HMF AEPHE Sunset Fireworks Advertisement Dark ocean Street Fountain Average The DECM values shown in Table 2 indicate that the AEPHE, 2DHE and AMHE improve the global discrete entropy and local contrast measures better than all other algorithms. For some images, the 2DHE and AMHE obtain higher DECM values than the AEPHE because 2DHE redistributes the original histogram more uniformly via 2D histogram while simultaneously boosting the contrast value for the local patch. However, the AEPHE achieves a higher average DECM than the 2DHE and AMHE. Table 3 shows that BPDFHE produces the satisfactory brightness preservation. The AEPHE outperforms all the other algorithms in brightness preservation, except for the BPDFHE, MMBEBHE, AMHE and RMSHE. In some cases, the brightness preservation may contradict with image enhancement, for example, in order to enhance dark images, the brightness should be boosted, but the AMBE may be reduced as a result. 5 Conclusion This paper proposes an AEPHE for dark image enhancement. In AEPHE, the EPHS strategy is developed to isolate the original histogram, which helps to sufficiently boost the dark regions. Moreover, to suppress unnatural artefacts, over-enhancement or under-enhancement in the enhanced image, this paper first develops the intensity preservation measure and contrast boosting measure to represent the grey distribution properties of each piecewise histogram. Based on these two measures, an adaptive HE is further developed to balance the intensity preservation and contrast boosting. The experimental results indicate that the AEPHE sufficiently enhances the dark regions and simultaneously avoid excessive enhancement or unnatural looking artefacts for the wide dynamic range dark images. 6 Acknowledgments This work was supported by the National High Technology Research and Development Program of China 863 Program, grant no. 2012AA112312), National Natural Science Foundation of China grant nos , and ), the Science and Technique Project of Ministry of Transport of the People s Republic of China grant no A70) and Hunan Provincial Natural Science Foundation of China 14JJ2052). 7 References 1 Rao, Y., Chen, L.: A survey of video enhancement techniques, J. Inf. Hiding Multimed. Signal Process., 2012, 3, 1), pp Rao, Y., Lin, W., Chen, L.: Image-based fusion for video enhancement of night-time surveillance, Opt. Eng. Lett., 2010, 49, 2), pp Lee, E., Kim, S., Kang, W., Seo, D., Paik, J.: Contrast enhancement using dominant brightness level analysis and adaptive intensity transformation for remote sensing images, IEEE Geosci. Remote Sens. Lett., 2013, 10, 1), pp Rivera, A.R., Ryu, B., Chae, O.: Content-aware dark image enhancement through channel division, IEEE Trans. Image Process., 2012, 21, 9), pp Provenzi, E., Gatta, C., Fierro, M., Rizzi, A.: A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, 10), pp Panetta, K.A., Wharton, E.J., Agaian, S.S.: Human visual system-based image enhancement and logarithmic contrast measure, IEEE Trans. Syst. Man Cyber. B., 2008, 38, 1), pp Chulwoo, L., Chul, L., Chang-Su, K.: Gradient domain contrast enhancement with histogram-guided boundary conditions. Proc. of 18th IEEE Int. Conf. on Image Processing ICIP), Brussels, Belguim, September 2011, pp Celik, T., Tjahjadi, T.: Contextual and variational contrast enhancement, IEEE Trans. Image Process., 2011, 20, 12), pp Shih-Chia, H., Fan-Chieh, C., Yi-Sheng, C.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution, IEEE Trans. Image Process., 2013, 22, 3), pp Gonzalez, R., Woods, R.: Digital image processing Prentice-Hall, 2007, 3rd edn.), pp & The Institution of Engineering and Technology 2015

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