Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3026703
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)
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Funding
- Agency for Defense Development of Korea [UD190005DD]
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In this study, a learning-based compressive sensing algorithm is proposed for denoising sonar images. The method combines deep learning and iterative shrinkage and thresholding algorithm, and incorporates CoordConv to help remove nonhomogeneous noise. Experimental results show that the proposed method outperforms existing methods in terms of noise removal and memory requirements.
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.
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