A Practical Solution for SAR Despeckling with Only Single Speckled Images.

In this letter, we aim to address synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, a practical solution for SAR despeckling (PSD) is proposed. Firstly, to generate speckled-to-speckled (S2S) image pairs from the same scene in the situation of only single speckled SAR images are available, an adversarial learning framework is designed. Then, the S2S SAR image pairs are employed to train a modified despeckling Nested-UNet model using the Noise2Noise (N2N) strategy. Moreover, an iterative version of the PSD method (PSDi) is also proposed. The performance of the proposed methods is demonstrated by both synthetic speckled and real SAR data. SAR block-matching 3-D algorithm (SAR-BM3D) and SAR dilated residual network (SAR-DRN) are used in the visual and quantitative comparison. Experimental results show that the proposed methods can reach a good tradeoff between speckle suppression and edge preservation.