Snapshot Compressive Imaging is an emerging technology that is based on compressive sensing theory to achieve high-efficiency hyperspectral data acquisition. The core problem of this technology is how to reconstruct 3D hyperspectral data from the 2D snapshot measurement in a fast and high-quality manner. In this paper, we propose a novel deep network, which consists of the symmetric residual module and the non-local spatial-spectral attention module, to learn the reconstruction mapping in a data-driven way. The symmetric residual module uses symmetric residual connections to improve the potential of interaction between convolution operations and further promotes the fusion of local features. The non-local spatial-spectral attention module is designed to capture the non-local spatial-spectral correlation in the hyperspectral image. Specifically, this module calculates the channel attention matrix to capture the global correlations between all of the spectral channels, and it fuses the channel attention attained feature maps and the spatial attention weighted features as the module output, thus both of the spatial-spectral correlations of hyperspectral images can be fully utilized for reconstruction. In addition, a compound loss, including the reconstruction loss, the measurement loss, and the cosine loss, is designed to guide the end-to-end network learning. We experimentally evaluate the proposed method on simulation and real datasets. The experimental results show that the proposed network outperforms the competing methods in terms of the reconstruction quality and running time.

中文翻译：

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具有非局部空间谱残差网络的高光谱快照压缩成像

快照压缩成像是一种基于压缩传感理论的新兴技术，可实现高效的高光谱数据采集。该技术的核心问题是如何以快速，高质量的方式从2D快照测量中重建3D高光谱数据。在本文中，我们提出了一种由对称残差模块和非局部空间光谱关注模块组成的新型深度网络，以数据驱动的方式学习重建映射。对称残差模块使用对称残差连接来提高卷积操作之间交互的可能性，并进一步促进局部特征的融合。非局部空间光谱关注模块被设计为捕获高光谱图像中的非局部空间光谱相关性。具体来说，该模块计算通道注意力矩阵以捕获所有光谱通道之间的全局相关性，并将通道关注度获得的特征图与空间关注加权特征融合为模块输出，从而将高光谱图像的空间光谱相关性可以充分利用来进行重建。此外，还设计了一种复合损耗，包括重构损耗，测量损耗和余弦损耗，以指导端到端网络学习。我们在仿真和真实数据集上实验性地评估了所提出的方法。实验结果表明，所提出的网络在重建质量和运行时间方面均优于竞争方法。