  Hyperspectral pansharpening is a process of merging a high-resolution
panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to
create a single high-resolution hyperspectral (HRHS) image. Existing
Bayesian-based HS pansharpening methods require designing handcraft image prior
to characterize the image features, and deep learning-based HS pansharpening
methods usually require a large number of paired training data and suffer from
poor generalization ability. To address these issues, in this work, we propose
a low-rank diffusion model for hyperspectral pansharpening by simultaneously
leveraging the power of the pre-trained deep diffusion model and better
generalization ability of Bayesian methods. Specifically, we assume that the
HRHS image can be recovered from the product of two low-rank tensors, i.e., the
base tensor and the coefficient matrix. The base tensor lies on the image field
and has a low spectral dimension. Thus, we can conveniently utilize a
pre-trained remote sensing diffusion model to capture its image structures.
Additionally, we derive a simple yet quite effective way to pre-estimate the
coefficient matrix from the observed LRHS image, which preserves the spectral
information of the HRHS. Experimental results demonstrate that the proposed
method performs better than some popular traditional approaches and gains
better generalization ability than some DL-based methods. The code is released
in https://github.com/xyrui/PLRDiff.
