Snapshot Compressive Imaging via Degradation Cue and Spectral Latent Diffusion

The goal of snapshot spectral compressive imaging reconstruction is to recover the 3D hyperspectral image from a 2D measurement. However, current reconstruction methods still face significant challenges in fully leveraging degradation and image prior. Many methods estimate degradation solely from a single measurement rather than learning from the real imaging process, resulting in inaccurate prior modeling. Moreover, the high compression of the CASSI measurement leads to the loss of spectral-spatial context, and the existing priors fail to fully capture it - for instance, in complex scenarios (such as S5, S9 in Table I), the performance gap can be as high as 3 dB. To address these issues, this paper introduces a novel reconstruction method with Degradation Cue Learning and Spectral Latent Diffusion (DCL-SLD), which comprises two key components: the Degradation Cue Learning (DCL) module and the Spectral Latent Diffusion (SLD) module. In the spatial domain, the DCL module employs a pre-trained image encoder and a feature distribution transmission strategy to extract degraded information and integrate it into the feature, enabling reconstruction through learned visual context. In the spectral domain, the SLD module leverages a latent diffusion model based on spectral correlations to generate a low-rank vector representation, effectively preserving contextual relationships within the high-dimensional structure. By enhancing priors in both dimensions, the model significantly improves its ability to exploit contextual information for more accurate recovery. Extensive experimental results on both simulation and real datasets demonstrate the superior performance of DCL-SLD over state-of-the-art methods.