STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising
Journal:
arXiv
Published Date:
May 30, 2025
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.
