Latent Diffusion Prior Enhanced Deep Unfolding for Spectral Image
  Reconstruction
Zongliang Wu Ruiying Lu Ying Fu Xin Yuan
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.