Coded Aperture Snapshot Spectral Imaging (CASSI) is a crucial technique for capturing three-dimensional multispectral images (MSIs) through the complex inverse task of reconstructing these images from coded two-dimensional measurements. Current state-of-the-art methods, predominantly end-to-end, face limitations in reconstructing high-frequency details and often rely on constrained datasets like KAIST and CAVE, resulting in models with poor generalizability. In response to these challenges, this paper introduces a novel one-step Diffusion Probabilistic Model within a self-supervised adaptation framework for Snapshot Compressive Imaging (SCI). Our approach leverages a pretrained SCI reconstruction network to generate initial predictions from two-dimensional measurements. Subsequently, a one-step diffusion model produces high-frequency residuals to enhance these initial predictions. Additionally, acknowledging the high costs associated with collecting MSIs, we develop a self-supervised paradigm based on the Equivariant Imaging (EI) framework. Experimental results validate the superiority of our model compared to previous methods, showcasing its simplicity and adaptability to various end-to-end or unfolding techniques.

本研究针对当前瞬时压缩成像技术在高频细节重建和模型泛化能力方面的不足，提出了一种新颖的自我监督适应框架中的一步扩散概率模型。实验结果表明，该模型在生成多光谱图像的重建上比现有方法更为优越，具有更高的简便性和适应性。

瞬时压缩成像的高效一步扩散精细化