NSC-SSNet: A Self-Supervised Network With Neighborhood Subsampling and Calibration Constraints for Sonar Image Denoising

Abstract

Sonar imaging systems play a crucial role in several marine applications. However, complex underwater environment introduces scattering noise, significantly degrading sonar image quality and hindering performance for downstream tasks. Although several self-supervised denoising methods have emerged to address the lack of clean reference images, they often fail to effectively capture both local and global structural information, thus showing suboptimal performance on sonar images. To address these challenges, we propose NSC-SSNet, a self-supervised network with neighborhood subsampling and calibration constraints for sonar image denoising. In particular, NSC-SSNet adopts an end-to-end self-supervised framework that operates in the denoising and calibration stages. By leveraging neighborhood subsampling and calibration constraints, it effectively extracts latent features of clean images from noisy input. Moreover, it simultaneously captures local and global associations between pixels by incorporating additional terms in the loss function to improve image quality while denoising. Extensive experiments on real-world sonar image datasets demonstrate that NSC-SSNet outperforms existing self-supervised denoising methods in terms of both noise removal and quality enhancement.