In low-visibility marine environments characterized by turbidity and
darkness, acoustic cameras serve as visual sensors capable of generating
high-resolution 2D sonar images. However, acoustic camera images are interfered
with by complex noise and are difficult to be directly ingested by downstream
visual algorithms. This paper introduces a novel strategy for denoising
acoustic camera images using deep learning techniques, which comprises two
principal components: a self-supervised denoising framework and a fine
feature-guided block. Additionally, the study explores the relationship between
the level of image denoising and the improvement in feature-matching
performance. Experimental results show that the proposed denoising strategy can
effectively filter acoustic camera images without prior knowledge of the noise
model. The denoising process is nearly end-to-end without complex parameter
tuning and post-processing. It successfully removes noise while preserving fine
feature details, thereby enhancing the performance of local feature matching.

本文介绍了一种使用深度学习技术对声学相机图像进行去噪的新策略，该策略由两个主要组件组成：自监督去噪框架和精细特征引导块。实验结果表明，该去噪策略可以有效过滤声学相机图像，而不需要先验的噪声模型。去噪过程几乎是端到端的，无需复杂的参数调整和后处理，成功去除噪声同时保留细节特征，从而增强了局部特征匹配的性能。