Abstract
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.
Abstract (translated)
我们研究了编码孔径 snapshot 光谱成像（CASSI）的逆问题，该逆问题通过捕获快照 2D 测量来构建一个空间 - 光谱数据立方，并使用算法来重构 3D 超分辨率图像（HSI）。然而，基于卷积神经网络（CNN）的现有方法很难捕捉长距离依赖关系和非局部相似性。最近流行的基于 Transformer 的方法在下游任务上表现不佳，因为自注意力引起的计算成本太高。在本文中，我们提出了粗略感知光谱感知平移卷积神经网络（CFSDCN），将平移卷积神经网络（DCN）应用于该任务，这是第一次这样做。考虑到 HSI 的稀疏性，我们设计了一个平移卷积模块，利用其可塑性来捕捉长距离依赖关系和非局部相似性。此外，我们提出了一种新的光谱信息交互模块，考虑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 数据集上显著优于最先进的（SOTA）方法。
URL
https://arxiv.org/abs/2406.12703
