Blur-Resistant Hyperspectral Image Super-Resolution via Dual-Degradation Fusion Model

Abstract

The deep unfolding network represents a promising research avenue in fusion-based hyperspectral image super-resolution (HSI-SR). However, most current deep unfolding methodologies are anchored in idealized observation models, which overlook the degradation of the multispectral image (MSI), hindering their SR performance and practical applicability. To address this problem, this paper establishes a novel Dual-Degradation Fusion (D2-Fusion) model, which incorporates both HSI degradation and MSI blurring into the HSI-SR modeling process. Subsequently, we apply the second-order semismooth Newton algorithm to solve the optimization problem in D2-Fusion model. The solution steps are then mapped into an end-to-end trainable network, termed Blur-resistant Hyperspectral image Super-Resolution Network (BHSR-Net). To the best of our knowledge, the proposed network is the first successful attempt to consider MSI blurring artifacts in the HSI-SR task. It offers several distinct advantages: 1) The network structure maintains a strict mathematical correspondence with the optimization algorithm, ensuring each module retains strong physical interpretability; 2) The network exhibits superior SR performance and strong generalization ability on both standard and real-world scenarios across five datasets; 3) The network demonstrates excellent learning efficiency with a compact architecture, and its lightweight variant achieves comparable results with only 38K parameters. The code is available at https://github.com/Dou0405/BHSR-Net