Genetic Algorithm based Bi level Histogram Modification Framework for Image Contrast Enhancement
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Histogram equalization is a well known technique used for contrast enhancement. However, it changes the brightness of an image and hence, not suitable for consumer electronic products. This paper proposes a new technique, Bi Level Histogram Modification using Genetic Algorithm (BLHM) for the purpose of enhancing the contrast as well as preserving details for any given input image. The basic idea of this technique is partition the input image histogram into two sub-histograms based on its mean. Then, a bi-criteria optimization problem is formulated to satisfy the aforementioned requirements and the two sub-histograms are modified by selecting the optimal contrast enhancement parameters. Finally, the union of two modified sub-histograms produces a contrast enhanced and details preserved output image. While formulating the optimization problem, Genetic Algorithm is employed to control the degree of enhancement. This mechanism enhances the contrast of the input image better than its existing contemporary HE methods. The performance of the proposed method is measured in terms of Discrete Entropy and Contrast Improvement Index.
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