Total variation blind deconvolution employing split Bregman iteration
- 作者：Weihong Li ; weihongli@cqu.edu.cn ; Quanli Li quanli2009@cqu.edu.cn ; Weiguo Gong ; Shu Tang
- 关键词：Image processing ; Blind deconvolution ; Split Bregman ; Total variation ; Deblurring ; Euler-Lagrange equation ; Iteration scheme ; Point spread function
- 刊名：Journal of Visual Communication and Image Representation
- 出版年：2012
- 期刊代码：141_10473203
- 类别：cp
- 出版时间：April, 2012
- 卷：23
- 期：3
- 页码：409-417
- 文件大小：1435 K
摘要
Blind image deconvolution is one of the most challenging problems in image processing. The total variation (TV) regularization approach can effectively recover edges of image. In this paper, we propose a new TV blind deconvolution algorithm by employing split Bregman iteration (called as TV-BDSB). Considering the operator splitting and penalty techniques, we present also a new splitting objective function. Then, we propose an extended split Bregman iteration to address the minimizing problems, the latent image and the blur kernel are estimated alternately. The TV-BDSB algorithm can greatly reduce the computational cost and improve remarkably the image quality. Experiments are conducted on both synthetic and real-life degradations. Comparisons are also made with some existing blind deconvolution methods. Experimental results indicate the advantages of the proposed algorithm.