  We study the inverse problem of Coded Aperture Snapshot Spectral Imaging
(CASSI), which captures a spatial-spectral data cube using snapshot 2D
measurements and uses algorithms to reconstruct 3D hyperspectral images (HSI).
However, current methods based on Convolutional Neural Networks (CNNs) struggle
to capture long-range dependencies and non-local similarities. The recently
popular Transformer-based methods are poorly deployed on downstream tasks due
to the high computational cost caused by self-attention. In this paper, we
propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN),
applying deformable convolutional networks (DCN) to this task for the first
time. Considering the sparsity of HSI, we design a deformable convolution
module that exploits its deformability to capture long-range dependencies and
non-local similarities. In addition, we propose a new spectral information
interaction module that considers both coarse-grained and fine-grained spectral
similarities. Extensive experiments demonstrate that our CFSDCN significantly
outperforms previous state-of-the-art (SOTA) methods on both simulated and real
HSI datasets.
