Hyperspectral images (HSI) have a large amount of spectral information reflecting the characteristics of matter, while their spatial resolution is low due to the limitations of imaging technology. Complementary to this are multispectral images (MSI), e.g., RGB images, with high spatial resolution but insufficient spectral bands. Hyperspectral and multispectral image fusion is a technique for acquiring ideal images that have both high spatial and high spectral resolution cost-effectively. Many existing HSI and MSI fusion algorithms rely on known imaging degradation models, which are often not available in practice. In this paper, we propose a deep fusion method based on the conditional denoising diffusion probabilistic model, called DDPM-Fus. Specifically, the DDPM-Fus contains the forward diffusion process which gradually adds Gaussian noise to the high spatial resolution HSI (HrHSI) and another reverse denoising process which learns to predict the desired HrHSI from its noisy version conditioning on the corresponding high spatial resolution MSI (HrMSI) and low spatial resolution HSI (LrHSI). Once the training is completes, the proposed DDPM-Fus implements the reverse process on the test HrMSI and LrHSI to generate the fused HrHSI. Experiments conducted on one indoor and two remote sensing datasets show the superiority of the proposed model when compared with other advanced deep learningbased fusion methods. The codes of this work will be opensourced at this address: https://github.com/shuaikaishi/DDPMFus for reproducibility.

翻译：高光谱图像（HSI）包含大量反映物质特征的光谱信息，但由于成像技术的限制，其空间分辨率较低。与之互补的是多光谱图像（MSI），例如RGB图像，具有高空间分辨率但光谱波段不足。高光谱与多光谱图像融合是一种以经济高效的方式获取同时具备高空间和高光谱分辨率理想图像的技术。许多现有HSI与MSI融合算法依赖已知的成像退化模型，但在实际应用中这些模型往往不可得。本文提出一种基于条件去噪扩散概率模型的深度融合方法，称为DDPM-Fus。具体而言，DDPM-Fus包含前向扩散过程（逐步向高空间分辨率HSI添加高斯噪声）和反向去噪过程（学习从含噪版本中预测期望的高分辨率HSI，并以对应的高空间分辨率MSI（HrMSI）和低空间分辨率HSI（LrHSI）为条件）。完成训练后，DDPM-Fus对测试的HrMSI和LrHSI执行反向过程，生成融合后的高空间分辨率HSI。在室内及两个遥感数据集上的实验表明，与其他基于深度学习的先进融合方法相比，该模型具有优越性。为促进可重复性研究，本文代码将开源于此地址：https://github.com/shuaikaishi/DDPMFus。