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Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper.
The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted.
A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement.
Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently.
The proposed algorithm is compared with some state-of-the-art HE-based algorithms.
The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed.
The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms.
The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.
American Psychological Association (APA)
Zhuang, Liyun& Guan, Y.-P.. 2018. Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130678
Modern Language Association (MLA)
Zhuang, Liyun& Guan, Y.-P.. Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1130678
American Medical Association (AMA)
Zhuang, Liyun& Guan, Y.-P.. Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1130678
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130678