A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries

• URL: http://arxiv.org/abs/2406.02914v1
• Date: Wed, 5 Jun 2024 04:07:37 GMT
• Title: A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries
• Authors: Xiaoteng Zhou, Katsunori Mizuno, Yilong Zhang,
• Abstract summary: This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching.
• Score: 3.0918473503782042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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