A Double Residual Iterative Regularization Method for SAR Image Despeckling
Chao Wang, Baolong Guo · IEEE Geoscience and Remote Sensing Letters · 2023
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.