Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging

• URL: http://arxiv.org/abs/2311.14280v2
• Date: Sat, 24 Aug 2024 01:37:10 GMT
• Title: Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging
• Authors: Zongliang Wu, Ruiying Lu, Ying Fu, Xin Yuan,
• Abstract summary: Snapshot spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. We introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to deep unfolding method.
• Score: 17.511583657111792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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