Abstract

Image contrast enhancement plays a vital role in various applications of digital image processing field like face recognition, satellite imaging and medical imaging. This paper proposes an efficient algorithm to cater the limitation of over enhancement with maximum entropy preservation. In the proposed algorithm, input image histogram is segmented first based on its valley positions and then weighted distribution is applied to all segmented sub histograms followed by the histogram equalization, gamma correction and homomorphic filtering. Results reveal that the proposed technique outer performs other conventional histogram equalization techniques both in terms of visual quality along with maximum entropy preservation and contrast enhancement.

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