Self-Supervised SAR Despeckling Powered by Implicit Deep Denoiser Prior

Speckle removal is an important preprocessing step for synthetic aperture radar (SAR) imaging. Since speckle-free SAR images do not exist, supervised methods are not applicable. In this letter, we propose implicit deep denoiser prior (SAR-IDDP), a self-supervised method for SAR despeckling. SAR-IDDP uses a deep image prior (DIP) implicitly captured by the convolutional neural network (CNN) to formulate regularization instead of traditional hand-crafted priors. Specifically, we treat the output of the CNN as a “prior” that we denoise again after “renoising.” The CNN is updated to maximize the similarity between the again denoised image and its prior. The renoising procedure is designed based on the assumption of unit mean noise, while the spatial correlation of speckle is also involved. The despeckling ability of our method stems from CNN’s natural tendency to capture low-level image statistics. Experiments show that SAR-IDDP achieves significant improvements over existing model-based and self-supervised despeckling methods on both synthetic and real SAR images.