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                  Automatic Contrast Limited Adaptive Histogram Equalization with Dual Gamma Correction

                  Yakun Chang1, Cheolkon Jung1, Peng Ke1, Hyoseob Song2, Jungmee Hwang2

                  1Xidian University

                  2Samsung SDS

                  Fig. 1.CLAHE with dual gamma correction. Top: Adjusted transform function by dual gamma correction. Bottom: Contrast enhancement result.

                  Abstract

                  We propose automatic contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement. We automatically set the clip point for CLAHE based on textureness of a block. Also, we introduce dual gamma correction into CLAHE to achieve contrast enhancement while preserving naturalness. First, we redistribute the histogram of the block in CLAHE based on the dynamic range of each block. Second, we perform dual gamma correction to enhance the luminance, especially in dark regions while reducing over-enhancement artifacts. Since automatic CLAHE adaptively enhances contrast in each block while boosting luminance, it is very effective in enhancing dark images and daylight ones with strong dark shadows. Moreover, automatic CLAHE is computationally efficient, i.e., more than 35 frames/s at 1024 × 682 resolution, due to the independent block processing for contrast enhancement. Experimental results demonstrate that automatic CLAHE with dual gamma correction achieves good performance in contrast enhancement and outperforms state-of-the-art methods in terms of visual quality and quantitative measures.

                  Paper

                  DOI: http://dx.doi.org/10.1109/ACCESS.2018.2797872

                  Citation:
                  Yakun Chang, Cheolkon Jung, Peng Ke, Hyoseob Song, Jungmee Hwang, “Automatic Contrast Limited Adaptive Histogram Equalization with Dual Gamma Correction,” IEEE Access, vol. 6, pp. 11782 – 11792, 2018.

                  Executable Files:

                  Download1

                  Test images & videos:

                  Download2

                  Acknowledgement

                  This work was supported by the National Natural Science Foundation of China (No. 61271298) and the International S&T Cooperation Program of China (No. 2014DFG12780).

                  ?2015 Xidian Media Lab. All rights reserved.
                  Email: jushutaoxidian@163.com

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