SonarGAN: A Progressive GAN Framework for Sonar Image Denoising under Multi-Type Noises

Abstract

Forward-looking sonar is essential for underwater perception especially in turbid waters, yet its images are often strongly degraded by various noises, including speckle, sidelobe, and structural noises, which severely hinder downstream tasks such as underwater reconstruction, positioning, and navigation. Most conventional sonar denoising methods reduce the noise at the expense of loss of fine image features or blurred image, while modern supervised learning methods demand large paired datasets that are impractical to obtain in real underwater conditions. In this paper, we propose SonarGAN, a progressive Generative Adversarial Networks (GAN) based framework that denoises sonar images under multi-type noises in one go. Unlike traditional supervised methods, SonarGAN avoids the need for costly paired datasets by combining unpaired real and simulated images, synthetic noisy–clean pairs, and joint refinement for comprehensive denoising. Extensive experiments across multiple types of sonar and underwater environments demonstrate the effectiveness of SonarGAN and its generalization in real-world conditions.