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Naturalness preservation image contrast enhancement via histogram modification

Tian, Qi-Chong; Cohen, Laurent D. (2017), Naturalness preservation image contrast enhancement via histogram modification, Proceedings of Ninth International Conference on Graphic and Image Processing, Proceedings of SPIE, the International Society for Optical Engineering. 10.1117/12.2302955

Type
Communication / Conférence
Date
2017
Conference title
Ninth International Conference on Graphic and Image Processing
Conference date
2017
Conference city
Qingdao
Conference country
China
Book title
Proceedings of Ninth International Conference on Graphic and Image Processing
Publisher
Proceedings of SPIE, the International Society for Optical Engineering
Publication identifier
10.1117/12.2302955
Metadata
Show full item record
Author(s)
Tian, Qi-Chong
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Cohen, Laurent D.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
Contrast enhancement is a technique for enhancing image contrast to obtain better visual quality. Since many existing contrast enhancement algorithms usually produce over-enhanced results, the naturalness preservation is needed to be considered in the framework of image contrast enhancement. This paper proposes a naturalness preservation contrast enhancement method, which adopts the histogram matching to improve the contrast and uses the image quality assessment to automatically select the optimal target histogram. The contrast improvement and the naturalness preservation are both considered in the target histogram, so this method can avoid the over-enhancement problem. In the proposed method, the optimal target histogram is a weighted sum of the original histogram, the uniform histogram, and the Gaussian-shaped histogram. Then the structural metric and the statistical naturalness metric are used to determine the weights of corresponding histograms. At last, the contrast-enhanced image is obtained via matching the optimal target histogram. The experiments demonstrate the proposed method outperforms the compared histogram-based contrast enhancement algorithms.
Subjects / Keywords
Image enhancement; Image contrast enhancement; Image quality; RGB color model; Image processing

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