DiLAST: Leveraging Differential RGB Features for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution aims to reconstruct high-quality spatial–spectral cubes from RGB images. However, the limited spectral coverage of RGB inputs hinders the simultaneous modeling of spatial structures and spectral relationships. To address this limitation, we propose a differential low-rank adaptive spatial–spectral Transformer (DiLAST). Initially, a differential operator is employed to enhance RGB features in a bottom–up manner, explicitly amplifying subtle interchannel differences. The enhanced features are, then, fed into a U-shaped backbone, which integrates three complementary modules for joint spatial–spectral modeling. Specifically, a center spatial–spectral attention (CSSA) module employs cross-attention mechanisms to capture local-to-global dependencies across both spatial and spectral domains; an adaptive cross-scale fusion (ACF) module utilizes learnable gating weights to establish dynamic interaction pathways between shallow high-frequency details and deep semantic representations; and a low-rank spectral calibration (LRSC) module exploits low-rank matrix priors to reveal low-dimensional manifold structures among spectral bands, thereby enhancing spectral consistency. By leveraging the synergistic effects of spatial nonlocality, global spectral correlation, and low-rank properties, the proposed DiLAST achieves peak signal-to-noise ratio (PSNR) improvements of 33.82, 36.03, and 37.01 dB on benchmark datasets. Moreover, the accuracy and practical applicability of the reconstructed spectra have been effectively validated in remote sensing scenarios and object tracking tasks. The code is accessible at https://github.com/renqi1998/DiLAST