Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base model to be binarized. Then we present the basic unit, Binarized Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively redistribute the HSI representations before binarizing activation and uses a scalable hyperbolic tangent function to closer approximate the Sign function in backpropagation. Based on our BiSR-Conv, we customize four binarized convolutional modules to address the dimension mismatch and propagate full-precision information throughout the whole network. Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model. Comprehensive quantitative and qualitative experiments manifest that our proposed BiSRNet outperforms state-of-the-art binarization methods and achieves comparable performance with full-precision algorithms. Code and models are publicly available at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MST

翻译：现有的高光谱图像重建深度学习模型性能优异，但需要配备大内存和高算力的硬件设备，导致这些方法难以部署在资源受限的移动设备上。本文提出一种新型二值化光谱重分布网络（BiSRNet），用于快照压缩成像系统中基于压缩测量的高效实用型高光谱图像复原。首先，我们重新设计了紧凑且易于部署的基础模型用于二值化处理；其次，提出基础单元——二值化光谱重分布卷积（BiSR-Conv）。该模块能在激活函数二值化之前自适应地重分布高光谱表示，并采用可缩放双曲正切函数更近似地逼近反向传播中的符号函数。基于BiSR-Conv，我们定制了四种二值化卷积模块，用于解决维度失配问题并维持全精度信息在全网络中的传播。最终，通过应用上述技术对基础模型进行二值化处理，得到BiSRNet。全面的定量与定性实验证明，我们提出的BiSRNet优于现有最先进的二值化方法，且性能可与全精度算法媲美。代码和模型已公开于https://github.com/caiyuanhao1998/BiSCI 和 https://github.com/caiyuanhao1998/MST