🤖 AI Summary
This work addresses the challenges in hyperspectral image denoising, particularly the difficulty of dynamically balancing data fidelity and noise priors, as well as inadequate modeling of mixed noise. To overcome these issues, the authors propose a spatial-spectral adaptive denoising framework that employs an adaptive weight tensor to dynamically adjust the trade-off between the fidelity term and regularization. The method integrates a lightweight noise prior, a pixel-wise robust model, and coefficient total variation regularization to effectively preserve both the low-rank structure and local smoothness of the image. An optimization strategy based on the Alternating Direction Method of Multipliers (ADMM) ensures a favorable balance between computational efficiency and reconstruction accuracy. Extensive experiments on both synthetic and real-world datasets demonstrate state-of-the-art denoising performance.
📝 Abstract
The core challenge of hyperspectral image denoising is striking the right balance between data fidelity and noise prior modeling. Most existing methods place too much emphasis on the intrinsic priors of the image while overlooking diverse noise assumptions and the dynamic trade-off between fidelity and priors. To address these issues, we propose a denoising framework that integrates noise prior reduction and a spatial-spectral adaptive fidelity term. This framework considers comprehensive noise priors with fewer parameters and introduces an adaptive weight tensor to dynamically balance the fidelity and prior regularization terms. Within this framework, we further develop a fast and robust pixel-wise model combined with the representative coefficient total variation regularizer to accurately remove mixed noise in HSIs. The proposed method not only efficiently handles various types of noise but also accurately captures the spectral low-rank structure and local smoothness of HSIs. An efficient optimization algorithm based on the alternating direction method of multipliers is designed to ensure stable and fast convergence. Extensive experiments on simulated and real-world datasets demonstrate that the proposed model achieves superior denoising performance while maintaining competitive computational efficiency.