  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.
