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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 6/2017

23-07-2016 | Original Article

A segmentation-based adaptive image enhancement method inspired by the self-adjust features of HVS

Authors: Hui Yin, Guohao Lyu, Xiaoyue Luo, Chenglong Li

Published in: International Journal of Machine Learning and Cybernetics | Issue 6/2017

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Abstract

This paper focuses on the enhancement of the images captured in insufficient or non-uniform lighting conditions. Inspired by the self-adjust features of HVS (Human Vision System), a Segmentation-Based Adaptive Image Enhancement (SAIE) method is proposed to improve the visibility of degraded images. First the local characteristics of different parts of each image (called sub-block images) obtained by image segmentation are described by Gaussian curvature, then the parameters of Gaussian filter for each sub-block image are chosen adaptively according to its local characteristics to estimate the illumination component of the whole image accurately. After that, adaptive dynamic range adjustment for the estimated illumination based on global characteristics and contrast enhancement for sub-block images are implemented. Finally, the enhancement result is obtained after color adjustment. The experiments on five typical image sets show that SAIE can provide effective and robust enhancement results. Comparative qualitative and quantitative evaluations with MSRCR, IRME and MSRCR+AL are also provided to demonstrate the efficiency and superiority of the proposed method.
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Metadata
Title
A segmentation-based adaptive image enhancement method inspired by the self-adjust features of HVS
Authors
Hui Yin
Guohao Lyu
Xiaoyue Luo
Chenglong Li
Publication date
23-07-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 6/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0567-2

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