Recurrent Operator Transport with Spectral Adaptation for Unregistered Hyperspectral Image Super-Resolution

Unregistered hyperspectral image (HSI) super-resolution transfers high-resolution spatial detail across misaligned sensors while preserving spectral fidelity. Existing methods typically transfer detail through cross-modal feature fusion, leaving the spatial reconstruction process implicit in the interaction between reference and HSI features. In this paper, we propose Recurrent Operator Transport with Spectral Adaptation (**ROTSA**), a framework that explicitly learns spatial reconstruction operators from the reference and transfers them to HSI restoration. Specifically, a self-reconstructing reference branch models cross-scale spatial transformations using neighborhood weights and an affine detail state, capturing local propagation relationships and missing high-frequency detail. Structural correspondence then transports these operators to the HSI grid, where they recurrently guide spatial restoration. Besides, to adapt the transferred correction to scene-dependent spectral characteristics, we further introduce adaptive spectral capacity, which represents the correction in a complete spectral basis estimated from the low-resolution HSI and continuously adjusts its contribution across the basis. This preserves the reconstruction supported by the observed HSI while allowing reference guidance to adapt to the spectral content of each scene. Experiments on simulated and real datasets demonstrate the effectiveness of the proposed method.