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New regularization scheme for blind color image deconvolution

[+] Author Affiliations
Li Chen

Wuhan University of Science and Technology, School of Computer Science and Technology, WuHan, China 430081

Yu He

Software Group, Protec. Co. Ltd., Singapore 609966

Kim-Hui Yap

Nanyang Technological University, School of Electrical and Electronic Engineering, Nanyang Avenue Singapore 639798

J. Electron. Imaging. 20(1), 013017 (March 22, 2011). doi:10.1117/1.3554414
History: Received April 12, 2010; Revised January 13, 2011; Accepted January 24, 2011; Published March 22, 2011; Online March 22, 2011
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This paper proposes a new regularization scheme to address blind color image deconvolution. Color images generally have a significant correlation among the red, green, and blue channels. Conventional blind monochromatic deconvolution algorithms handle each color image channels independently, thereby ignoring the interchannel correlation present in the color images. In view of this, a unified regularization scheme for image is developed to recover edges of color images and reduce color artifacts. In addition, by using the color image properties, a spectral-based regularization operator is adopted to impose constraints on the blurs. Further, this paper proposes a reinforcement regularization framework that integrates a soft parametric learning term in addressing blind color image deconvolution. A blur modeling scheme is developed to evaluate the relevance of manifold parametric blur structures, and the information is integrated into the deconvolution scheme. An optimization procedure called alternating minimization is then employed to iteratively minimize the image- and blur-domain cost functions. Experimental results show that the method is able to achieve satisfactory restored color images under different blurring conditions.

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Citation

Li Chen ; Yu He and Kim-Hui Yap
"New regularization scheme for blind color image deconvolution", J. Electron. Imaging. 20(1), 013017 (March 22, 2011). ; http://dx.doi.org/10.1117/1.3554414


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