A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this\npaper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the\nhistogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function\nof the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrastenhanced\nimage is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some\nstate-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are\nstatistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing\ncontrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the\nmean brightness and details well preserved.
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