STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising

• URL: http://arxiv.org/abs/2505.24327v1
• Date: Fri, 30 May 2025 08:09:31 GMT
• Title: STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
• Authors: Jingjing Liu, Jiashun Jin, Xianchao Xiu, Jianhua Zhang, Wanquan Liu,
• Abstract summary: We propose a novel RSI denoising method named sparse tensor-aided representation network (STAR-Net)<n>We extend STAR-Net to a sparse variant called STAR-Net-S to deal with the interference caused by non-Gaussian noise in original RSI for the purpose of improving robustness.<n> Comprehensive experiments on synthetic and real-world datasets demonstrate that STAR-Net and STAR-Net-S outperform state-of-the-art RSI denoising methods.
• Score: 19.319042523414183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing image (RSI) denoising is an important topic in the field of remote sensing. Despite the impressive denoising performance of RSI denoising methods, most current deep learning-based approaches function as black boxes and lack integration with physical information models, leading to limited interpretability. Additionally, many methods may struggle with insufficient attention to non-local self-similarity in RSI and require tedious tuning of regularization parameters to achieve optimal performance, particularly in conventional iterative optimization approaches. In this paper, we first propose a novel RSI denoising method named sparse tensor-aided representation network (STAR-Net), which leverages a low-rank prior to effectively capture the non-local self-similarity within RSI. Furthermore, we extend STAR-Net to a sparse variant called STAR-Net-S to deal with the interference caused by non-Gaussian noise in original RSI for the purpose of improving robustness. Different from conventional iterative optimization, we develop an alternating direction method of multipliers (ADMM)-guided deep unrolling network, in which all regularization parameters can be automatically learned, thus inheriting the advantages of both model-based and deep learning-based approaches and successfully addressing the above-mentioned shortcomings. Comprehensive experiments on synthetic and real-world datasets demonstrate that STAR-Net and STAR-Net-S outperform state-of-the-art RSI denoising methods.

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