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Proceedings Paper

A robust multi-frame image blind deconvolution algorithm via total variation
Author(s): Haiyang Zhou; Guo Xia; Qianshun Liu; Feihong Yu
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Paper Abstract

Image blind deconvolution is a more practical inverse problem in modern imaging sciences including consumer photography, astronomical imaging, medical imaging, and microscopy imaging. Among all of the latest blind deconvolution algorithms, the total variation based method provides privilege for large blur kernel. However, the computation cost is heavy and it does not handle the estimated kernel error properly. Otherwise, the using of the whole image to estimate the blur kernel is inaccurate because of that the insufficient edges information will hazard the accuracy of estimation. Here, we proposed a robust multi-frame images blind deconvolution algorithm to handle this complicated imaging model and applying it to the engineering community. In our proposed method, we induced the patch and kernel selection scheme to selecting the effective patch to estimate the kernel without using the whole image; then an total variation based kernel estimation algorithm was proposed to estimate the kernel; after the estimation of blur kernels, a new kernel refinement scheme was applied to refine the pre-estimated multi-frame estimated kernels; finally, a robust non-blind deconvolution method was implemented to recover the final latent sharp image with the refined blur kernel. Objective experiments on both synthesized and real images evaluate the efficiency and robustness of our algorithm and illustrate that this approach not only have rapid convergence but also can effectively recover high quality latent image from multi-blurry images.

Paper Details

Date Published: 8 October 2015
PDF: 6 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750I (8 October 2015); doi: 10.1117/12.2197528
Show Author Affiliations
Haiyang Zhou, Zhejiang Univ. (China)
Guo Xia, Zhejiang Univ. (China)
Qianshun Liu, Zhejiang Univ. (China)
Feihong Yu, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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