IrregFusion: A Generalized Framework for Hyperspectral Image Fusion Across Diverse Spectral Data

Fusing a low-resolution (LR) hyperspectral image (HSI) with a high-resolution (HR) multispectral image (MSI) has emerged as a promising strategy for reconstructing high-quality HSIs that combine rich spectral and fine spatial information. However, most existing HSI fusion methods operate under the restrictive assumption that the LR HSI and HR MSI are spatially aligned and fully consistent on the field-of-view (FoV), which significantly limits their applicability in real-world scenarios when such alignment is unavailable. To overcome these limitations, we propose IrregFusion, a generalized HSI fusion framework capable of handling both FoV-consistent and inconsistent fusion scenarios. In particular, IrregFusion incorporates a Transformer-based reconstruction module (illustrated in Fig. 2) that captures both intramodal and intermodal correlations between the diverse spectra data and the MSI, enhancing the model's ability to perceive and reconstruct nonlocal spectral-spatial structures. To further address the challenges posed by FoV inconsistencies, we introduce a spectral propagation strategy that diffuses observed spectral information into adjacent spectral-blank regions, thereby easing the reconstruction of missing spectral content. In addition, a self-supervised adaptation mechanism is integrated into the framework, enabling robust spectral-spatial representation learning and enhancing generalization across diverse and challenging conditions. Extensive experiments conducted on benchmark datasets demonstrate that IrregFusion effectively addresses the challenges of diverse spectral data fusion and consistently outperforms state-of-the-art (SOTA) methods in both reconstruction accuracy and visual fidelity. The source code will be released in https://github.com/JiangtaoNie/IrregFusion.git