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 will be released at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MST

翻译：现有用于高光谱图像重建的深度学习模型性能优异，但需要强大硬件支持，消耗大量内存与计算资源，因此难以部署在资源受限的移动设备上。本文提出一种新方法——二值化光谱再分布网络（BiSRNet），用于从快照压缩成像系统的压缩测量值中实现高效实用的高光谱图像恢复。首先，我们重新设计了一个紧凑且易于部署的基础模型以进行二值化；随后提出基础单元——二值化光谱再分布卷积（BiSR-Conv）。该模块能在激活值二值化前自适应地重新分布高光谱图像表示，并采用可缩放双曲正切函数来更近似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，以解决维度不匹配问题并在整个网络中传播全精度信息。最终，通过上述技术对基础模型进行二值化，得到BiSRNet。全面的定量与定性实验表明，所提BiSRNet优于现有最先进的二值化方法，且性能与全精度算法相当。代码与模型将开源至https://github.com/caiyuanhao1998/BiSCI 与https://github.com/caiyuanhao1998/MST。