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Bi-histogram equalization using modified histogram bins

Published: 01 June 2017 Publication History

Abstract

Display Omitted The proposed BHEMHB improves conventional histogram equalization.Histogram segmentation enables mean brightness preservation.Histogram modification reduces domination effect of high-frequency histogram bins.BHEMHB is tested using standard and cervical cell images.Statistical analyses reveal improvement in entropy, PSNR and AMBE measurements. 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.

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      Published In

      cover image Applied Soft Computing
      Applied Soft Computing  Volume 55, Issue C
      June 2017
      621 pages

      Publisher

      Elsevier Science Publishers B. V.

      Netherlands

      Publication History

      Published: 01 June 2017

      Author Tags

      1. Histogram equalization
      2. Histogram segmentation
      3. Image enhancement
      4. Information entropy
      5. Mean brightness

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