Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data release_kvqfge2vejet5o4p3dlsncqnly

by Xiaoteng Zhou, Changli Yu, Xin Yuan, Citong Luo

Released as a article .

2021  

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.
In text/plain format

Archived Files and Locations

application/pdf  2.1 MB
file_hmp4rzeujffrvb2wsu2elcwiwu
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-08-27
Version   v1
Language   en ?
arXiv  2108.12083v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: e76cfbef-4b24-428c-a749-ace36604992b
API URL: JSON