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This letter presents a method of synthetic aperture radar (SAR) imagedespeckling aimed to preserve the detail information while suppressing specklenoise. This method combines the nonlocal self-similarity partition and aproposed modified sparse decomposition. The nonlocal partition method groups aseries of structure-similarity data sets. Each data set has a good sparsity forlearning an over-complete dictionary in sparse representation. In the sparsedecomposition, we propose a novel method to identify principal atoms fromover-complete dictionary to form a principal dictionary. Despeckling isperformed on each data set over the principal dictionary with principal atoms.Experimental results demonstrate that the proposed method can achieve highperformances in terms of both speckle noise reduction and structure detailspreservation.

Sar image despeckling based on nonlocal similarity sparse decomposition