SEGSID: A Semantic-Guided Framework for Sonar Image Despeckling

Shaohua Liu, Junzhe Lu, Hongkun Dou, Jiajun Li, Yue Deng

Abstract: Sonar imagery is substantially degraded by speckle noise, making the task of despeckling crucial for improving image quality. Self-supervised despeckling methods, represented by blind-spot networks (BSNs), have shown promise in this regard. However, these methods consistently face significant challenges due to the spatial correlation of speckle noise and the inherent information loss within BSNs. In this paper, we introduce SEGSID, a BSN-based, semantic-guided sonar despeckling framework designed to address these challenges. Specifically, the SEGSID framework primarily comprises a Receptive Field Augmentation (RFA) module and a Global Semantic Enhancement (GSE) module. To address the noise spatial correlation, the RFA module is crafted to strategically extract valuable local information while avoiding the exploitation of noise-correlated pixels. Concurrently, the GSE module extracts the global semantic information from entire images and injects it into the extracted local features. This enhances BSNs’ ability to harness more comprehensive image information and compensates for their inherent information loss. Furthermore, to bolster efficiency, we employ knowledge distillation techniques to transfer the expertise from the trained SEGSID into a more streamlined network suitable for broader practical applications. Extensive experiments on three distinct sonar datasets demonstrate that SEGSID outperforms both traditional despeckling methods and state-of-the-art self-supervised despeckling techniques. The implementation is publicly accessible at https://github.com/deng-ai-lab/SEGSID.