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虽性能优异，但需要强大硬件支持，消耗大量内存与计算资源。因此，这些方法难以部署在资源受限的移动设备上。本文提出一种新型方法——双值化光谱再分配网络（BiSRNet），用于从快照压缩成像系统的压缩测量中高效恢复高光谱图像。首先，我们重新设计了紧凑且易于部署的基模型以进行双值化处理。随后提出基本单元——双值化光谱再分配卷积（BiSR-Conv）。该模块可在激活双值化前自适应重新分配高光谱表示，并采用可缩放的双曲正切函数在反向传播中更逼近符号函数。基于BiSR-Conv，我们定制了四种双值化卷积模块以解决维度不匹配问题，并在全网络中传播全精度信息。最终通过上述技术对基模型进行双值化得到BiSRNet。全面的定性与定量实验表明，所提BiSRNet优于现有最优双值化方法，且性能与全精度算法相当。代码与模型已开源至https://github.com/caiyuanhao1998/BiSCI 及https://github.com/caiyuanhao1998/MST。