Self-supervised low-light hyperspectral image enhancement via Fourier-based transformer network

Abstract

Low-light hyperspectral images (HSIs) suffer from reduced visibility, amplified noise, and distorted spectral signatures, which degrade critical downstream tasks in surveillance, environmental monitoring, and remote sensing. Because collecting paired normal/low-light HSIs is often impractical, we introduce SS-HSLIE, the first self-supervised framework for low-light HSI enhancement. Guided by Retinex theory, our cascaded network (i) decomposes an input HSI into reflectance and illumination maps and (ii) refines the illumination with a Transformer module that models global spatial context. Two physics-aware losses further steer learning: a Fourier spectrum loss that removes noise while protecting high-frequency details, and a spectral smoothness loss that preserves inter-band consistency. Trained solely on unpaired low-light data, SS-HSLIE substantially outperforms recent unsupervised baselines on both an indoor benchmark and a challenging new real-world outdoor dataset, delivering brighter, cleaner HSIs while faithfully preserving material-specific spectra. Code, pretrained models, and our new outdoor HSI dataset will be released.