  This letter presents a method of synthetic aperture radar (SAR) image
despeckling aimed to preserve the detail information while suppressing speckle
noise. This method combines the nonlocal self-similarity partition and a
proposed modified sparse decomposition. The nonlocal partition method groups a
series of structure-similarity data sets. Each data set has a good sparsity for
learning an over-complete dictionary in sparse representation. In the sparse
decomposition, we propose a novel method to identify principal atoms from
over-complete dictionary to form a principal dictionary. Despeckling is
performed on each data set over the principal dictionary with principal atoms.
Experimental results demonstrate that the proposed method can achieve high
performances in terms of both speckle noise reduction and structure details
preservation.
