Side scan sonar image super-resolution using an improvedinitialization structure

Junyeop Lee, Bon Hwa Ku, Wan Jin Kim, Hanseok Ko

Research output: Contribution to journalArticlepeer-review


This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.

Original languageEnglish
Pages (from-to)121-129
Number of pages9
JournalJournal of the Acoustical Society of Korea
Issue number2
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Acoustical Society of Korea. All rights reserved.


  • Compressive sensing
  • Deep learning
  • Super resolution
  • Underwater sonar image

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Acoustics and Ultrasonics
  • Applied Mathematics
  • Speech and Hearing


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