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Contrast enhancement using feature-preserving bi-histogram equalization

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Abstract

A new contrast enhancement algorithm is proposed, which is based on the fact that, for conventional histogram equalization, a uniform input histogram produces an equalized output histogram. Hence before applying histogram equalization, we modify the input histogram in such a way that it is close to a uniform histogram as well as the original one. Thus, the proposed method can improve the contrast while preserving original image features. The main steps of the new algorithm are adaptive gamma transform, exposure-based histogram splitting, and histogram addition. The object of gamma transform is to restrain histogram spikes to avoid over-enhancement and noise artifacts effect. Histogram splitting is for preserving mean brightness, and histogram addition is used to control histogram pits. Extensive experiments are conducted on 300 test images. The results are evaluated subjectively as well as by DE, PSNR EBCM, GMSD, and MCSD metrics, on which, except for the PSNR, the proposed algorithm has some improvements of 2.89, 9.83, 28.32, and 26.38% over the second best ESIHE algorithm, respectively. That is to say, the overall image quality is better.

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Acknowledgements

This project is partially supported by the National Natural Science Foundation of China (61362021, 61272216, 61572147), Guangxi Natural Science Foundation (2013GXNSFDA019030, 2014GXNSFAA118003, 2016GXNSFAA380043), Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics (GIIP201408, GIIP201503, GIIP201501, GIIP201401), Basic Capabilities Promotion Project for Youth and Middle-aged Teachers in Colleges and Universities of Guangxi (ky2016YB162), and Program for Innovative Research Team of Guilin University of Electronic Technology.

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Authors and Affiliations

  1. School of Computer Science and Information Security, Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, China

    Xuewen Wang

  2. School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China

    Lixia Chen

  3. Guangxi Experiment Center of Information Science, Guilin, China

    Xuewen Wang & Lixia Chen

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  1. Xuewen Wang
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  2. Lixia Chen
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Correspondence to Lixia Chen.

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Wang, X., Chen, L. Contrast enhancement using feature-preserving bi-histogram equalization. SIViP 12, 685–692 (2018). https://doi.org/10.1007/s11760-017-1208-2

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  • DOI: https://doi.org/10.1007/s11760-017-1208-2

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