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A weighted l0 shearlet-based method for image deblurring
Author(s): Guomin Sun; Jinsong Leng; Man Li
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Paper Abstract

This paper proposed a weighted l0 shearlet-based model for image deblurring. The main purpose of this work is to further exploiting the sparsity of the reconstructed signal. In order to achieve this goal, a generalized gradient regularizer is introduced to the model. The added regularizer can suppress artifacts effectively. The split Bregman algorithm is used to update the multi-scale weighted matrix in the each iteration. This weighted matrix can transmit the solution information in the present step to the next step by support detection. According to this procedure, the whole algorithm framework forms a learning process. Experimental results suggest that the proposed algorithm yields significantly improvement in terms of PSNR. However, it also shows that more computing time is required due to the utilization of the redundant shearlet system.

Paper Details

Date Published: 21 September 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042022 (21 September 2017); doi: 10.1117/12.2281762
Show Author Affiliations
Guomin Sun, Univ. of Electronic Science and Technology of China (China)
Jinsong Leng, Univ. of Electronic Science and Technology of China (China)
Man Li, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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