A Spectral Diffusion Prior for Unsupervised Hyperspectral Image Super-Resolution

Jianjun Liu, Zebin Wu, Liang Xiao

Abstract: Hyperspectral image (HSI) super-resolution with an auxiliary multispectral image (MSI) belongs to the class of inverse problems, where prior knowledge is essential for obtaining the target. Various hand-crafted or deep priors have been developed to enforce the desired solutions. Nevertheless, the spectral distribution knowledge is ignored and still not exploited as a prior. To this end, we design a spectral diffusion model (SDM) to capture the spectral distribution of HSIs and thereby exploit it as a prior for the problem of unsupervised HSI super-resolution. Specifically, we first investigate the spectrum generation problem and extend the diffusion model to fit the 1-D spectral data. Then, we transfer the spectral distribution knowledge of the trained SDM by means of keeping its transition information and thereby induce a regularization term in the framework of maximum a posteriori. At last, we integrate the iterative solving and diffusion generation processes together and employ the Adam to solve the final optimization problem by following the reverse spectral generative sequence. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The code of the proposed approach is available on https://github.com/liuofficial/SDP .