@inproceedings{
zhang2024vmambasci,
title={Vmamba{SCI}: Dynamic Deep Unfolding Network with Mamba for Compressive Spectral Imaging},
author={Mingjin Zhang and Longyi Li and Wenxuan SHI and Jie Guo and Yunsong Li and Xinbo Gao},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=MNvMDbaQXa}
}
Snapshot spectral compressive imaging can capture spectral information across multiple wavelengths in one imaging. The method, coded aperture snapshot spectral imaging (CASSI), aims to recover 3D spectral cubes from 2D measurements. Most existing methods employ deep unfolding framework based on Transformer, which alternately address a data subproblem and a prior subproblem. However, these frameworks lack flexibility regarding the sensing matrix and inter-stage interactions. In addition, the quadratic computational complexity of global Transformer and the restricted receptive field of local Transformer impact reconstruction efficiency and accuracy. In this paper, we propose a dynamic deep unfolding network with mamba for compressive spectral imaging, called VmambaSCI. We integrate spatial-spectral information of the sensing matrix into the data module and employs spatial adaptive operations in the stage interaction of the prior module. Furthermore, we develop a dual-domain scanning mamba (DSMamba), featuring a novel spatial-channel scanning method for enhanced efficiency and accuracy. To our knowledge, this is the first Mamba-based model for compressive spectral imaging. Experimental results on the public database demonstrate the superiority of the proposed VmambaSCI over the state-of-the-art approaches.
VmambaSCI: Dynamic Deep Unfolding Network with Mamba for Compressive Spectral Imaging
Mingjin Zhang Longyi Li Wenxuan SHI Jie Guo Yunsong Li Xinbo Gao