Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging
<|reference_start|>Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging: 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.<|reference_end|>
@article{wang2024efficient,
    title={Efficient One-Step Diffusion Refinement for Snapshot Compressive Imaging},
    author={Yunzhen Wang, Haijin Zeng, Shaoguang Huang, Hongyu Chen, and Hongyan
  Zhang},
    journal={arXiv preprint arXiv:2409.07417},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07417},
    primaryClass={eess.IV cs.CV}
}