Denoising ISTA-Net: Learning based compressive sensing with reinforced non-linearity for side scan sonar image denoising

Bokyeung Lee, Bonwha Ku, Wan Jin Kim, Seongil Kim, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a learning based compressive sensing algorithm for the purpose of side scan sonar image denoising. The proposed method is based on Iterative Shrinkage and Thresholding Algorithm (ISTA) framework and incorporates a powerful strategy that reinforces the non-linearity of deep learning network for improved performance. The proposed method consists of three essential modules. The first module consists of a non-linear transform for input and initialization while the second module contains the ISTA block that maps the input features to sparse space and performs inverse transform. The third module is to transform from non-linear feature space to pixel space. Superiority in noise removal and memory efficiency of the proposed method is verified through various experiments.

Original languageEnglish
Pages (from-to)246-254
Number of pages9
JournalJournal of the Acoustical Society of Korea
Volume39
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • Compressive sensing
  • Image denoising
  • Learning based compressive sensing
  • Side scan sonar

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Denoising ISTA-Net: Learning based compressive sensing with reinforced non-linearity for side scan sonar image denoising'. Together they form a unique fingerprint.

Cite this