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Bi-histogram equalization using modified histogram bins
Affiliation:1. University of Milano-Bicocca, Department of Informatics, Systems and Communication, Milano 20126, Italy;2. Institute of Molecular Bioimaging and Physiology, Italian National Research Council, Cefalú (PA) 90015, Italy;3. SYSBIO.IT Centre of Systems Biology, Milano 20126, Italy;4. University of Bergamo, Department of Human and Social Sciences, Bergamo 24129, Italy
Abstract:The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.
Keywords:Histogram equalization  Histogram segmentation  Image enhancement  Mean brightness  Information entropy
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