21 September 2017 A weighted l0 shearlet-based method for image deblurring
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042022 (2017) https://doi.org/10.1117/12.2281762
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Guomin Sun, Jinsong Leng, Man Li, "A weighted l0 shearlet-based method for image deblurring", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042022 (21 September 2017); doi: 10.1117/12.2281762; https://doi.org/10.1117/12.2281762
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