Synthetic aperture radar (SAR) imaging systems are greatly affected by speckle noise, which brings great difficulties to the postprocessing of SAR images. By characterizing edge and nonlocal self-similarity features simultaneously,… Click to show full abstract
Synthetic aperture radar (SAR) imaging systems are greatly affected by speckle noise, which brings great difficulties to the postprocessing of SAR images. By characterizing edge and nonlocal self-similarity features simultaneously, this letter proposes a double residual iterative regularization (DRIR) despeckling method. To this end, the Fisher-Tippett (FT) distribution based despeckling model is first introduced. Next, to exploit the edge feature in a more reasonable way, a nonconvex total variation (NTV) regularization model based on FT distribution is proposed, and the solution to the resulting nonconvex optimization problem is given. Then, a despeckling model based on weighted Schatten $p$ -norm is proposed, which can characterize the nonlocal self-similarity feature more flexibly. Finally, extensive experimental results demonstrate that the proposed method can effectively remove speckles while preserving edges and textures compared with some state-of-the-art methods.
               
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