  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
