Sonar image denoising based on Noise2Void self-supervised learning

作者：Yongqiang Ji, Lan Xie, Gaozheng Xu, Zheng Zhu, Yu Gu, Fei Jie, Yuwei Li, Yao Chen, Kuijie Cai · 发表于：Remote Sensing Letters · 年份：2025 · DOI：10.1080/2150704x.2025.2480760 · 被引用次数：3 · 研究领域：Image and Signal Denoising Methods、Advanced Image Fusion Techniques、Image Processing Techniques and Applications

Sonar imaging plays a key role in underwater detection, and sonar denoising is essential for obtaining high-resolution images. Additive Gaussian noise and multiplicative speckle noise are two main sources which are widely distributed in sonar images and significantly degrading their quality. Due to the high exploration cost and complexity of the exploration environment of sonar images, traditional deep learning methods cannot be well applied to sonar images. In this study, we applied the Noise2Void method to train a U-Net for sonar image denoising. This method uses a self-supervised training method, which is well suited for sonar image denoising. It has two main advantages: Firstly, the method can suppress both the additive Gaussian noise and the multiplicative speckle noise, while most of the methods developed are effective for one type of noise; Secondly, the method can be trained with a single noisy image, while most of the supervised deep-learning methods are unsuitable for sonar-image denoising owing to limitations such as the acquisition cost of sonar images and lack of clean labelled data. We tested the proposed method on the SeabedObjects-KLGS dataset. Noise2Void demonstrates comparable or even superior performance to conventional methods in denoising, regardless of whether the noise is speckle or Gaussian in nature.