Speckle2Self: Learning Self-Supervised Despeckling with Attention Mechanism for SAR Images
Huiping Lin, Xin Su, Zhiqiang Zeng, Cheng Xing, Junjun Yin · Remote Sensing · 2025
Despite the in-depth understanding of the synthetic aperture-radar (SAR) speckle and its characteristics, despeckling remains an open issue far from being solved. Deep-learning methods with supervised training have made great progress. However, reliable reference images are inconveniently accessible or even non-existent. In this paper, we propose an end-to-end self-supervised method named Speckle2Self for SAR image despeckling, which learns mapping from noisy input to clean output using only the input noisy image itself for training. We formulate the image despeckling as a masked pixel-estimation problem, where a set of masks is carefully designed. The masked pixel values are predicted by the queries of complementary masks indicating the positions of masked pixels through an attention mechanism. Transformer architecture is employed as the network backbone. In addition, a novel loss function is also derived based on the statistics of SAR images, and meanwhile, image downsampling is used to provide guarantees on the white noise assumption involved in our Speckle2Self. We compare the proposed Speckle2Self with reference methods on both synthetic and real images. Experimental results demonstrate that the proposed Speckle2Self achieves comparable despeckling performance with supervised methods, suppressing noise while maintaining structural details. Even compared with self-supervised methods, the proposed Speckle2Self still has significant advantages in SAR image-despeckling metrics.