Novel mean-shift based histogram equalization using textured regions |
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Authors: | Yu-Ren LaiKuo-Liang Chung Chyou-Hwa ChenGuei-Yin Lin Chao-Hsin Wang |
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Affiliation: | a Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10672, Taiwan, ROC b Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10672, Taiwan, ROC |
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Abstract: | This paper presents a novel mean-shift based histogram equalization method called the MSHE method. The key insight of the proposed MSHE method is that the basis of histogram equalization could be based on textured regions in an image, while impact of smoother regions should be suppressed. Using a mean-shift based approach, the sets of textured regions in an image are determined by finding regions which have a high density of edge concentration. In addition, a new cost function is presented to balance the image quality and contrast enhancement effect for search termination in the proposed algorithm. Based on three typical test images, experimental results show that our proposed MSHE method is quite competitive with the previous eleven methods, such as the HE, BBHE, DSIHE, POHE, RSWHE, DHE, BPDHE, SRHE, GHE, FHE, and THShap. |
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Keywords: | Contrast enhancement Cost function Histogram equalization Machine learning Mean-shift method Textured regions |
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