DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral
  Diffusion Model
Zhenghao Pan Haijin Zeng Jiezhang Cao Kai Zhang Yongyong Chen
This paper endeavors to advance the precision of snapshot compressive imaging
(SCI) reconstruction for multispectral image (MSI). To achieve this, we
integrate the advantageous attributes of established SCI techniques and an
image generative model, propose a novel structured zero-shot diffusion model,
dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior
and optimization-based methodologies, complemented by the generative
capabilities offered by the contemporary denoising diffusion model.
Specifically, firstly, we employ a pre-trained diffusion model, which has been
trained on a substantial corpus of RGB images, as the generative denoiser
within the Plug-and-Play framework for the first time. This integration allows
for the successful completion of SCI reconstruction, especially in the case
that current methods struggle to address effectively. Secondly, we
systematically account for spectral band correlations and introduce a robust
methodology to mitigate wavelength mismatch, thus enabling seamless adaptation
of the RGB diffusion model to MSIs. Thirdly, an accelerated algorithm is
implemented to expedite the resolution of the data subproblem. This
augmentation not only accelerates the convergence rate but also elevates the
quality of the reconstruction process. We present extensive testing to show
that DiffSCI exhibits discernible performance enhancements over prevailing
self-supervised and zero-shot approaches, surpassing even supervised
transformer counterparts across both simulated and real datasets. Our code will
be available.