  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.
