An Enhanced Noise Removal-based SAR Image Recognition Using DnCNN and Wavelet Transform
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Date
2025-09
Authors
Choi, Y.
Kim, G.
Kim, B.
Kim, S.
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Radioengineering Society
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Abstract
This paper presents an enhanced method for noise removal and target detection in Synthetic Aperture Radar (SAR) images using a Denoising Convolutional Neural Network (DnCNN) combined with wavelet trans¬form. Unlike conventional method, the proposed frame¬work focuses on remove the Speckle Noise through residu¬al learning and wavelet transform. The DnCNN architecture, consisting of 29 layers, efficiently removes noise while preserving high-frequency image features. The integration of wavelet transform further enhances noise removal and feature preservation. Experimental results demonstrate that the recognition rate of the proposed method improves by about 94% compared to original method under 10 dB Speckle Noise conditions. This method outperforms conventional algorithm in SAR image pro¬cessing, making it highly suitable for applications in noisy environments.
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Citation
Radioengineering. 2025 vol. 34, č. 3, s. 429-437. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2025/25_03_0429_0437.pdf
https://www.radioeng.cz/fulltexts/2025/25_03_0429_0437.pdf
Document type
Peer-reviewed
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Published version
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Language of document
en