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 / ConférenceDate
2017Conference title
Ninth International Conference on Graphic and Image ProcessingConference date
2017Conference city
QingdaoConference country
ChinaBook title
Proceedings of Ninth International Conference on Graphic and Image ProcessingPublisher
Proceedings of SPIE, the International Society for Optical Engineering
Publication identifier
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Show full item recordAuthor(s)
Tian, Qi-ChongCEntre 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 processingRelated items
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