Denoising ISTA-Net: learning based compressive sensing with reinforced non-linearity for side scan sonar image denoising
- Authors
- Lee, Bokyeung; Ku, Bonwha; Kim, Wan-Jin; Kim, Seongil; Ko, Hanseok
- Issue Date
- 2020
- Publisher
- ACOUSTICAL SOC KOREA
- Keywords
- Side scan sonar; Image denoising; Compressive sensing; Learning based compressive sensing
- Citation
- JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, v.39, no.4, pp.246 - 254
- Indexed
- SCOPUS
KCI
- Journal Title
- JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA
- Volume
- 39
- Number
- 4
- Start Page
- 246
- End Page
- 254
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/59025
- DOI
- 10.7776/ASK.2020.39.4.246
- ISSN
- 1225-4428
- 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.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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