A review on SAR image denoising algorithm using deep learning theory

Zhen Dai; Guangwen Mu; Wei Zha; Renzheng Xu

doi:10.1117/12.3037578

22 October 2024 A review on SAR image denoising algorithm using deep learning theory

Zhen Dai, Guangwen Mu, Wei Zha, Renzheng Xu

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 1327419 (2024) https://doi.org/10.1117/12.3037578
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

In the process of generating Synthetic Aperture Radar (SAR) images, the noise based on speckle is produced due to physical reasons, and its presence seriously affects the interpretation and post-processing of the images. This study mainly focuses on the limitations of speckle noise in SAR images and summarizes the denoising algorithms in this field. Firstly, the SAR imaging principle is introduced, and the model of speckle noise generated during the imaging process is explained. Then, the WoS, ELSEZVIER, ScienceDirect, and Scopus databases are searched by using Boolean operators, and 30 denoising algorithms that used deep learning theory for SAR images in the past 5 years (2018-2022) are introduced and their relevant functions are counted. Finally, this study aims to provide ideas for the subsequent research on SAR image denoising by summarizing the current mainstream algorithms and their characteristics

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhen Dai, Guangwen Mu, Wei Zha, and Renzheng Xu "A review on SAR image denoising algorithm using deep learning theory", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 1327419 (22 October 2024); https://doi.org/10.1117/12.3037578

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