Sar image despeckling based on nonlocal similarity sparse decomposition

TL;DR

This paper introduces a SAR image despeckling method that combines nonlocal self-similarity grouping with a novel sparse decomposition technique to effectively reduce noise while preserving image details.

Contribution

It proposes a new despeckling approach that utilizes nonlocal similarity partitioning and a modified sparse decomposition to enhance noise suppression and detail preservation.

Findings

Effective speckle noise reduction demonstrated

Preserves structural details well

Outperforms existing methods in experiments

Abstract

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.