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

    Bibliographical note

    Publisher Copyright:
    Copyright © 2020 The Acoustical Society of Korea.

    Keywords

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

    ASJC Scopus subject areas

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

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