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 language | English |
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Pages (from-to) | 246-254 |
Number of pages | 9 |
Journal | Journal of the Acoustical Society of Korea |
Volume | 39 |
Issue number | 4 |
DOIs | |
Publication status | Published - 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