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Journal of Electronic Imaging

New regularization scheme for blind color image deconvolution
Author(s): Li Chen; Yu He; Kim-Hui Yap
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Paper Abstract

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.

Paper Details

Date Published: 1 January 2011
PDF: 13 pages
J. Electron. Imaging. 20(1) 013017 doi: 10.1117/1.3554414
Published in: Journal of Electronic Imaging Volume 20, Issue 1
Show Author Affiliations
Li Chen, Wuhan Univ. of Science and Engineering (China)
Yu He, Protec. Co., Ltd. (Singapore)
Kim-Hui Yap, Nanyang Technological Univ. (Singapore)


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