This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous attributes of established SCI techniques and an image generative model, propose a novel structured zero-shot diffusion model, dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies, complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction, especially in the case that current methods struggle to address effectively. Secondly, we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch, thus enabling seamless adaptation of the RGB diffusion model to MSIs. Thirdly, an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process. We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches, surpassing even supervised transformer counterparts across both simulated and real datasets. Our code will be available.

本研究通过将传统SCI技术和图像生成模型相结合，提出了一种名为DiffSCI的新型结构化零射扩散模型，来提高多光谱图像的快照压缩成像（SCI）重建的精度。DiffSCI利用了深度先验和基于优化的方法对结构的理解，加上当代去噪扩散模型提供的生成能力。我们的广泛测试表明DiffSCI在模拟和真实数据集上均比现有的自监督和零射扩散方法表现出明显的性能优势，甚至超过了受监督的转换器对应方法。

DiffSCI：基于迭代谱扩散模型的零样本快照压缩成像