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.

翻译：本研究针对编码孔径快照光谱成像（CASSI）的逆问题展开研究。CASSI通过单次快照二维测量捕获空间-光谱数据立方体，并利用算法重建三维高光谱图像（HSI）。然而，当前基于卷积神经网络（CNN）的方法难以捕捉长程依赖关系与非局部相似性。近期流行的基于Transformer的方法因自注意力机制导致的高计算成本，在下游任务中部署困难。本文首次将可变形卷积网络（DCN）应用于该任务，提出粗粒度-细粒度光谱感知可变形卷积网络（CFSDCN）。针对HSI的稀疏特性，我们设计了可变形卷积模块，利用其形变能力捕捉长程依赖与非局部相似性。此外，我们提出了一种新的光谱信息交互模块，同时考虑粗粒度与细粒度的光谱相似性。大量实验表明，在仿真与真实HSI数据集上，我们的CFSDCN方法均显著优于以往最先进的（SOTA）方法。