Decoupling spatial and spectral features for efficient hyperspectral image super-resolution

Abstract Hyperspectral image super-resolution (HSI-SR) aims to reconstruct hyperspectral images at high spatial resolution, starting from low-resolution inputs, while preserving both spatial details and spectral fidelity. In this work, we propose the Efficient Spatial-Spectral Processing Network (ESSPN), a lightweight deep learning architecture designed to address the challenges of HSI-SR in a computationally efficient way. ESSPN is built around a novel Spatial-Spectral Block (SSB) that separately models spatial structures and spectral correlations through residual convolutional and attention mechanisms. The network head incorporates an efficient upsampling module based on pixel shuffle decomposition to produce high-resolution outputs without interpolation artifacts. Extensive experiments on two publicly available datasets, i.e., ARAD1K and StereoMSI demonstrate that ESSPN achieves competitive or superior performance compared to state-of-the-art methods when evaluated at scale factors of $$\times $$ 4, $$\times $$ 6 and $$\times $$ 8. Notably, the model shows strong generalization across hyperspectral cameras with varying spectral responses, and across radiometric domains, covering both radiance and reflectance measurements, while requiring significantly fewer parameters and FLOPs compared to existing methods. These results position the proposed ESSPN as a practical and effective solution for high-quality hyperspectral image super-resolution in real-world applications.