Causal Degradation-Guided Network With Spatial–Frequency Attention for Blind Hyperspectral Image Fusion

Fusing the spectral information of low-resolution hyperspectral image (LR-HSI) with the spatial details of high-resolution multispectral image (HR-MSI) is an effective strategy for reconstructing high-resolution hyperspectral image (HR-HSI). Existing methods perform well when the degradation characteristics of training and test data are consistent. However, their generalization ability often deteriorates when faced with unknown degradations. In this paper, we treat the degradation conditions as confounders in the causal relationship, and introduce diverse degradation schemes to mitigate the spurious correlations of the fusion process, compelling the model to learn latent causal patterns independent of specific degradation mechanisms. For this, we propose a degradation-guided blind hyperspectral image fusion network with spatial-frequency attention to better reveal these underlying causal characteristics. Specifically, two U-shaped subnetworks adaptively extract multi-scale spatial and spectral degradation features from LR-HSI and HR-MSI, which are then embedded as guidance features into the reconstruction network for more accurate HR-HSI reconstruction. In addition, we design a spatial–frequency attention module, which integrates spatial window self-attention and frequency-domain attention in a parallel and interactive manner to fully exploit the complementary characteristics of the spatial and frequency domains. Extensive experiments on multiple public datasets demonstrate the effectiveness and superior generalization of the proposed method. The code of the proposed approach is available on https://github.com/liuofficial/CDGN.