A framework for super-resolution of side-scan sonar images: Combination of variational Bayes and regional feature selection

Side-scan sonar is widely used in ocean exploration due to its broad search range and strong identification capabilities. However, the inherent characteristics of acoustic images often result in poor image quality, negatively impacting subsequent downstream tasks’ accuracy. Image super-resolution (SR) technology based on deep learning technology is employed to address this issue. Despite this, existing SR models face two main challenges when applied to side-scan sonar images: (1) less data in side-scan sonar images causes the model overfitting problem; (2) less effective features in side-scan sonar images cause lower efficiency. To overcome these challenges, this paper proposes a deep learning framework that integrates a Bayesian structure with region-based feature selection. First, we introduce a rolling region selection method to extract key features of interest from side-scan sonar images, enhancing efficiency without compromising quality. Additionally, we replace traditional Convolutional Neural Networks (CNN) with Variational Bayes Convolutional Neural Networks (VB-CNN) to perform the SR task, improving generalization on small datasets and mitigating the risk of overfitting. Experiments conducted on the Side-Scan Sonar Visual Object Classes (SSS-VOC) dataset and other datasets demonstrate our proposed approach's effectiveness through both qualitative and quantitative comparisons.