Snapshot compressive spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. Existing state-of-the-art methods are mostly based on deep unfolding structures but have intrinsic performance bottlenecks: $i$) the ill-posed problem of dealing with heavily degraded measurement, and $ii$) the regression loss-based reconstruction models being prone to recover images with few details. In this paper, we introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to enhance the regression-based deep unfolding method. Furthermore, to overcome the large computational cost challenge in LDM, we propose a lightweight model to generate knowledge priors in deep unfolding denoiser, and integrate these priors to guide the reconstruction process for compensating high-quality spectral signal details. Numeric and visual comparisons on synthetic and real-world datasets illustrate the superiority of our proposed method in both reconstruction quality and computational efficiency. Code will be released.

翻译：快照压缩光谱成像重建旨在从单次二维压缩测量中重建三维空间-光谱图像。现有最先进方法大多基于深度展开结构，但存在固有性能瓶颈：1）处理严重退化测量的病态问题；2）基于回归损失的模型倾向于恢复细节缺失的图像。本文引入生成模型——潜扩散模型（LDM），用于生成无退化先验以增强基于回归的深度展开方法。此外，为克服LDM中较大的计算成本挑战，我们提出一种轻量级模型，在深度展开去噪器中生成知识先验，并整合这些先验引导重建过程以补偿高质量光谱信号细节。在合成与真实数据集上的数值及视觉对比表明，所提方法在重建质量与计算效率方面均具优越性。代码将公开。