Learning Generalization From Various Unaware Degradations for Blind Hyperspectral Image Super-Resolution via Transparent Diffusion Model

Hyperspectral image (HSI) super-resolution through the fusion of low-resolution HSI (LrHSI) and high-resolution multispectral image (HrMSI) has emerged as a critical technique for enhancing the quality of HSIs. The recent progress in this field predominantly assume a known mapping relationships between high-resolution HSI (HrHSI) and low-resolution version, relying on networks to learn this mapping to generate HrHSI. However, this assumption is often unrealistic in practical applications. To address this limitation, we propose the Spatial-Spectral-Integrated Transparent Diffusion Model (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>TD) for blind HSI-SR, which is more adaptive to scene-variant degradations with a universal framework for both spatial and spectral reconstruction. Specifically, we design a multi-order degradation pool to generate diverse samples, thereby reducing the distribution gap between low-resolution images in real scenes. Additionally, we develop a spatial-spectral consistent degradation model, which is iteratively solved using an optimization algorithm and unrolled into neural networks for separate restoration in spatial and spectral aspects. Furthermore, the capabilty of progressive reconstruction in the diffusion model is involved to fit various degradations in different dimensions using similar network architectures, thereby enhancing the overall robustness of the network to various and complex scenarios. Comprehensive experiments conducted on three publicly synthetic datasets and one real-world dataset validate the superior performance of the proposed method under the condition that the degradation remains unknown.