@inproceedings{d74257f9a18e4fa29896dc25d1037d95,
title = "TVTADMM-Net: An ADMM network based on total variation polarization texture denoising",
abstract = "Polarimetric Synthetic Aperture Radar (SAR) system can obtain richer scattering characteristics of ground objects. However, the inherent speckle noise in SAR images severely degrades their quality, and traditional denoising methods are difficult to apply to denoise polarimetric SAR images in complex scenarios, especially the texture details. Therefore, an ADMM network based on total variation (TV) polarization texture denoising is proposed in this paper. It preserves the backscatter coefficient properties of the feature target by {"}smoothing{"}the texture details. That is, the TV regularization term is added to the denoising process to construct a polarised texture sparse denoising model.",
keywords = "ADMM, denoising, Polarimetric SAR, total variation (TV)",
author = "Ziwen Wang and Xueting Shan and Yiyang Luo and Pucheng Li and Yifan Wu and Han Li",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024 ; Conference date: 22-11-2024 Through 24-11-2024",
year = "2024",
doi = "10.1109/ICSIDP62679.2024.10867900",
language = "English",
series = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024",
address = "United States",
}