Tensor Hourglass Decomposition for Hyperspectral and Multispectral Image Fusion

Hyperspectral image super-resolution (HSISR) aims to reconstruct a high-resolution hyperspectral image from a low-resolution hyperspectral image and a high-resolution multispectral image. A central challenge is to recover fine spatial details from the multispectral observation while maintaining consistency with the spectral information provided by the hyperspectral observation. To address this challenge, we propose a tensor hourglass decomposition (THD) model that separately characterizes heterogeneous spatial and spectral structures while preserving their high-order interactions. THD organizes the spatial and spectral representations into two closed peripheral cycles and couples their spoke modes through a shared high-order waist core, thereby enabling flexible many-to-many interactions between spatial structures and spectral signatures. By exploiting the multilinearity of THD, the spatial degradation and spectral response operators are directly absorbed into the corresponding physical-mode-carrying factors, yielding a structured coupled observation model for HSISR. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive or superior spatial–spectral reconstruction performance.