Feature Sparse Coding With CoordConv for Side Scan Sonar Image Enhancement

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...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Lee, Bokyeung, Ku, Bonhwa, Kim, Wanjin, Kim, Seungil, Ko, Hanseok
Format: Article
Language:English
Subjects:
Algorithms
Compressive sensing (CS)
Conditioned stimulus
Convolution
CoordConv
Deep learning
Image coding
image denoising
Image enhancement
Image resolution
Iterative algorithms
Machine learning
Noise
Noise intensity
Noise reduction
nonhomogeneous noise
Nonlinear systems
Nonlinearity
Side scan sonar
side scan sonar (SSS)
Sonar
Sonar imagery
Training
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Description
Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3026703