Unpaired image despeckling based on adversarial speckle generation

Xu Wang, Yanxia Wu, Ye Yuan, Yan Fu, Xue Zhang

Abstract: Speckle suppression is a critical step in a coherent imaging system. Currently, deep learning-based despeckling methods can be categorized as supervised and self-supervised. The former is unsuitable for this task due to the unavailability of speckle-free images, while the latter may fall short in despeckling performance due to the lack of prior knowledge constraints. To this end, we propose an unpaired image despeckling method based on adversarial speckle generation (UID-ASG), eliminating the need for manual design of image and speckle priors.UID-ASG emphasizes the joint distribution of speckled and clean images. It integrates self-supervised blind spot learning with adversarial learning to precisely simulate speckle distribution and utilizes unpaired speckle-clean image pairs for training. By merging the advantages of supervised and self-supervised methods, UID-ASG significantly enhances its despeckling performance. Experimental results demonstrate that UID-ASG outperforms other state-of-the-art methods in despeckling effectiveness.