HPDiff: Zero-Shot Blind Hyperspectral Pansharpening with Degradation-Guided Diffusion Sampling

Hyperspectral pansharpening reconstructs a high-resolution hyperspectral image (HSI) from a low-resolution HSI (LRHS) and a high-resolution panchromatic (PAN) image. Existing methods trained with simulated degradations often rely on predefined spatial point spread functions (PSFs) and spectral response functions (SRFs). When these assumptions mismatch unknown sensor degradations, the reconstruction may fail to satisfy both observations. To address this issue, we propose HPDiff, a zero-shot blind hyperspectral pansharpening method with degradation-guided diffusion sampling. HPDiff first estimates the unknown PSF and SRF from a single test pair by matching spatially degraded PAN with spectrally projected LRHS in a shared low-resolution PAN domain. The estimated operators then define LRHS/PAN data consistency gradients to guide the reverse sampling of a pretrained low-rank Diffusion Sampling. During joint reconstruction, HPDiff further refines the degradation parameters around their initial estimates, improving observation adaptation while retaining physical plausibility. Observation Prior Guidance (OPG) integrates LRHS spectral information, PAN spatial structures, and lightweight task-information priors to regulate spectral, spatial, and edge constraints during reverse sampling. A Coupled Spatial-Spectral Low-Rank Decoder (CSLD) jointly estimates interacting spatial and spectral factors to map diffusion details into a full-band HSI. Experiments on multiple benchmarks demonstrate improved PSNR, SAM, and ERGAS, together with better preservation of spatial structures and spectral consistency. The code will be made publicly available upon acceptance.