Exposure-based Weighted Dynamic Histogram Equalization for Image Contrast Enhancement | Zendy

Yung-Yao Chen | Zendy; Shin-Anne Chen | Zendy

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Exposure-based Weighted Dynamic Histogram Equalization for Image Contrast Enhancement

Author(s) -

Yung-Yao Chen,

Shin-Anne Chen

Publication year - 2015

Publication title -

international journal of automation and smart technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.148

H-Index - 10

ISSN - 2223-9766

DOI - 10.5875/ausmt.v5i1.835

Subject(s) - histogram equalization , adaptive histogram equalization , histogram , histogram matching , artificial intelligence , image histogram , balanced histogram thresholding , brightness , color normalization , pixel , computer science , pattern recognition (psychology) , computer vision , mathematics , contrast (vision) , image (mathematics) , image processing , color image , physics , optics

Global histogram equalization (GHE) [1] is a common method used for improving image contrast. However, this technique tends to introduce unnecessary visual artifacts and cannot preserve overall brightness. To overcome these problems, many ssudies have been conducted based on partitioned-histogram (i.e., sub-histogram) equalization. An input image is first divided into sub-images, individual histograms of the sub-images are equalized independently, and all of the sub-images are ultimately integrated into one complete image. For example, exposure-based sub-image histogram equalization (ESIHE) [2] uses an exposure-related threshold to divide the original image into different intensity ranges (horizontal partitioning) and also uses the mean brightness as a threshold to clip the histogram (vertical partitioning).

In this paper, a novel method, called exposure-based weighted dynamic histogram equalization (EWDHE), which is an extension of ESIHE, is proposed. This study makes three major contributions to the literature. First, an Otsu-based approach and a clustering performance measure are integrated to determine the optimal number of sub-histograms and the separating points. Second, an exposure-related parameter is used to automatically adapt the contrast limitation, to avoid over-enhancement in some portions of the image. Third, a new weighted scale factor is proposed to resize the sub-histograms, which accounts for sub-histogram ranges and individual pixel numbers of these ranges. The simulation results indicated that the proposed method outperformed state-of-the-art approaches in terms of contrast enhancement, brightness preservation, and entropy preservation.

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