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.

翻译：基于融合的高光谱图像超分辨率旨在通过融合低空间分辨率的高光谱图像与高空间分辨率的多光谱图像，生成高空间分辨率的高光谱图像。此类超分辨率过程可建模为逆问题，其中先验知识对于获取期望解至关重要。受扩散模型成功应用的启发，我们提出了一种新颖的频谱扩散先验，用于基于融合的高光谱图像超分辨率。具体而言，我们首先探究频谱生成问题，并设计一个频谱扩散模型以建模频谱数据分布。随后，在最大后验概率框架下，我们在逆向生成过程中保留每两个相邻状态间的转移信息，从而将训练好的频谱扩散模型知识以正则化项形式嵌入融合问题中。最终，我们将最终优化问题的每个生成步骤视为其子问题，并采用Adam算法以逆向序列求解这些子问题。在合成与真实数据集上的实验结果均证明了所提方法的有效性。所提方法的代码将发布于https://github.com/liuofficial/SDP。