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.

研究了编码光阑快照光谱成像 (CASSI) 的反问题，该方法使用快照 2D 测量来捕获空间 - 光谱数据立方体，并使用算法重建 3D 高光谱图像 (HSI)。然而，基于卷积神经网络 (CNN) 的当前方法难以捕捉长程依赖和非局部相似性。最近流行的基于 Transformer 的方法由于自我注意力引起的高计算成本在下游任务中得不到充分应用。在本文中，我们首次将可变卷积网络 (DCN) 应用于这项任务，提出了粗 - 细粒度光谱感知可变卷积网络 (CFSDCN)。考虑到 HSI 的稀疏性，我们设计了一个变形卷积模块，利用其可变形性来捕捉长程依赖和非局部相似性。此外，我们提出了一个新的光谱信息交互模块，考虑到粗粒度和细粒度的光谱相似性。大量实验证明，我们的 CFSDCN 在模拟和真实 HSI 数据集上显著优于之前的最先进方法。