A Deep Learning Based Denoising Approach for Synthetic Aperture Sonar Imaging

Xicheng Lu, Guanxi Sun, Wei Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Synthetic Aperture Sonar (SAS) is one of the techniques used for underwater imaging, and in this paper, we use the range Doppler algorithm for SAS imaging and consider the position errors of sonar platforms in practical scenarios. These errors can seriously degrade the imaging result; however, correcting them and performing compensation with high-precision equipment could be very costly. In this paper, a deep learning approach is proposed using artificial neural networks for image denoising, specifically targeting noise induced by motion uncertainties. As demonstrated by computer simulations, it can recover the images with better quality without employing complex algorithms and hardware compensation.

Original languageEnglish
Title of host publicationIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515669
DOIs
Publication statusPublished - Nov 2024
Event2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 - Zhuhai, China
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

Conference

Conference2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024
Country/TerritoryChina
CityZhuhai
Period22/11/2424/11/24

Keywords

  • artificial neural networks
  • deep learning
  • image denoising
  • SAS

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Control and Optimization

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