Browsing by Author "Wang, Wei-Kang"

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Image Contrast Enhancement Using Classified Virtual Exposure Image Fusion
(2012-11-01)
Lee, Chang-Hsing
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Chen, Ling-Hwei
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Wang, Wei-Kang
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資訊工程學系
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Department of Computer Science
In our daily life, digital cameras and smart phones have been widely used to take pictures. However, digital cameras and smart phones have a limited dynamic range, which is much lower than that human eyes can perceive. Thus, the photographs taken in high dynamic range scenes often exhibit under-exposure or over-exposure artifacts in shadow or highlight regions. In this study, an image fusion based approach, called classified virtual exposure image fusion (CVEIF), is proposed for image enhancement. First, a function imitating the F-stop concept in photography is designed to generate several virtual images having different intensity. Then, a classified image fusion method, which blends pixels in distinct luminance classes using different fusion functions, is proposed to produce a fused image in which every image region is well exposed. Experimental results on four different kinds of generic images, including a normal image, a low-contrast images, a backlight image, and a dark scene image, have shown that the proposed CVEIF approach produced more pleasingly enhanced images than other methods(1).
Image enhancement approach using the just-noticeable-difference model of the human visual system
(2012-07-01)
Lee, Chang-Hsing
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Lin, Pei-Ying
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Chen, Ling-Hwei
;
Wang, Wei-Kang
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多媒體工程研究所
The low-contrast images taken by digital cameras or camera phones are not always satisfactory due to the limitation of the capturing devices or improper illumination/exposure conditions. Conventional image contrast enhancement methods may either fail to produce satisfactory and undistorted images, or they cannot improve every region of interest appropriately, especially faces. In this paper, a histogram equalization (HE) approach exploiting the just-noticeable-difference (JND) model of the human visual system (HVS), denoted by JND-HE, is proposed for generic image contrast enhancement. Further, the proposed JND-HE approach is combined with the exposure correction (EC) method (denoted by JND-HE-EC) for face image enhancement. The proposed JND-HE-EC approach can improve the contrast in face regions and provide proper illumination in the background. Experimental results on both generic images and faces have shown that our proposed approach can produce more pleasing and appealing enhanced images than other methods. (C) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.3.033007]