Aperture Array-Based Self-Supervised Hyperspectral Super-Resolution Imaging

Current hyperspectral imaging (HSI) technologies struggle with prolonged acquisition time, bulky system design, and high computational complexity. We propose Aperture Array-Based Self-Supervised Hyperspectral Super-Resolution Imaging, a novel computational HSI framework that integrates an aperture array optical structure with a physics-driven, self-supervised deep learning model. The aperture array enables spectral division while leveraging aperture parallax for spatial super-resolution (SR), achieving high spectral fidelity and spatial SR in a compact form factor. To fully exploit the imaging characteristics of the proposed system, we design a Divide-and-Conquer Self-Supervised Hyperspectral Super-Resolution Imaging (DSHSI) algorithm, which is data-efficient, non-iterative, and free of large-scale ground-truth datasets. Unlike existing hyperspectral restoration methods that suffer from long inference times and poor generalization, DSHSI integrates physics-driven priors into self-supervised learning framework, enabling robust performance on unseen scenes and out-of-distribution degradations. DSHSI jointly performs denoising, SR, and deblurring in a unified manner, reducing runtime by 25×, FLOPs by 9× and model parameters by 3.6× than DualSR on public remote sensing datasets, while achieving a 1.9 dB PSNR gain, enabling fast and efficient hyperspectral reconstruction. In the optical experiments, it achieves the best NIQE and the spectral distortion metric that is closely aligned with the spectral response of observed HSI data. Our code is publicly available at https://github.com/THUHoloLab/DSHSR.