Feature Sparse Coding With CoordConv for Side Scan Sonar Image Enhancement
- Authors
- Lee, Bokyeung; Ku, Bonhwa; Kim, Wanjin; Kim, Seungil; Ko, Hanseok
- Issue Date
- 2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Noise reduction; Convolution; Image coding; Sonar; Image enhancement; Iterative algorithms; Image resolution; Compressive sensing (CS); CoordConv; image denoising; nonhomogeneous noise; side scan sonar (SSS)
- Citation
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, v.19
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
- Volume
- 19
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/136624
- DOI
- 10.1109/LGRS.2020.3026703
- ISSN
- 1545-598X
- Abstract
- In this letter, we propose a learning-based compressive sensing (CS) algorithm for denoising side scan sonar (SSS) images. The proposed method is a deep learning-based CS method with enhanced nonlinearity based on an iterative shrinkage and thresholding algorithm (ISTA). Since noise intensity varies depending on the position within SSS images, the proposed method also incorporates CoordConv, which provides coordinate information to the network to help remove nonhomogeneous noise. Through end-to-end training, both the deep learning module and the CS characteristics can be jointly optimized. Representative experimental results show that the proposed method is better than state-of-art methods in terms of both noise removal and memory requirements.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.