WTCRNet: a wavelet transform and contrastive regularization network for sonar denoising by self-supervision

Abstract

In the field of underwater acoustics, forward-looking sonar represents a pivotal tool for acquiring subaqueous imagery. However, this technique is susceptible to the inherent ambient noise prevalent in underwater environments, resulting in degraded image quality. A notable challenge in this domain is the scarcity of pristine image exemplars, making it difficult to apply many advanced deep denoising networks for the purification of sonar images. To address this issue, the study introduces a novel self-supervised methodology specifically designed for denoising forward-looking sonar images. The proposed model employs a blind-spot network architecture to reconstruct unblemished images. Additionally, it integrates wavelet transform technology within a convolutional neural network (CNN) framework, combining frequency and structural information. Furthermore, the model incorporates contrastive regularization to augment denoising efficiency. This innovative denoising network, which leverages wavelet transform and contrastive regularization (CR), is henceforth referred to as WTCRNet. To evaluate the performance of WTCRNet, this study constructs a dual dataset comprising both simulated and authentic forward-looking sonar images, thereby furnishing a comprehensive dataset for network training and evaluation. Empirical assessments conducted on these datasets demonstrate that WTCRNet substantially outperforms existing denoising methodologies by effectively mitigating noise. The code is available at https://gitee.com/sichengling/wtcrnet.git.