Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data

TL;DR

This paper introduces a deep learning-based denoising technique for side scan sonar images that operates without high-quality reference data, effectively reducing noise and preserving details in real seabed images.

Contribution

It presents a novel self-supervised deep denoising method that does not require high-quality reference images, outperforming traditional filtering techniques.

Findings

Effective noise reduction on real seabed SSS images

Preserves image details better than classical filters

Demonstrates advantages of deep learning in sonar image denoising

Abstract

Subsea images measured by the side scan sonars (SSSs) are necessary visual data in the process of deep-sea exploration by using the autonomous underwater vehicles (AUVs). They could vividly reflect the topography of the seabed, but usually accompanied by complex and severe noise. This paper proposes a deep denoising method for SSS images without high-quality reference data, which uses one single noise SSS image to perform self-supervised denoising. Compared with the classical artificially designed filters, the deep denoising method shows obvious advantages. The denoising experiments are performed on the real seabed SSS images, and the results demonstrate that our proposed method could effectively reduce the noise on the SSS image while minimizing the image quality and detail loss.