A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution
This work addresses a domain-specific problem in remote sensing and imaging by introducing a novel prior for hyperspectral image fusion, representing an incremental advancement in method design.
The paper tackles the problem of hyperspectral image super-resolution by fusing low-resolution hyperspectral and high-resolution multispectral images, proposing a spectral diffusion prior that improves performance, with experimental results on synthetic and real datasets demonstrating its effectiveness.
Fusion-based hyperspectral image (HSI) super-resolution aims to produce a high-spatial-resolution HSI by fusing a low-spatial-resolution HSI and a high-spatial-resolution multispectral image. Such a HSI super-resolution process can be modeled as an inverse problem, where the prior knowledge is essential for obtaining the desired solution. Motivated by the success of diffusion models, we propose a novel spectral diffusion prior for fusion-based HSI super-resolution. Specifically, we first investigate the spectrum generation problem and design a spectral diffusion model to model the spectral data distribution. Then, in the framework of maximum a posteriori, we keep the transition information between every two neighboring states during the reverse generative process, and thereby embed the knowledge of trained spectral diffusion model into the fusion problem in the form of a regularization term. At last, we treat each generation step of the final optimization problem as its subproblem, and employ the Adam to solve these subproblems in a reverse sequence. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach. The code of the proposed approach will be available on https://github.com/liuofficial/SDP.