Recently, deep neural networks have achieved excellent performance on
low-light raw video enhancement. However, they often come with high
computational complexity and large memory costs, which hinder their
applications on resource-limited devices. In this paper, we explore the
feasibility of applying the extremely compact binary neural network (BNN) to
low-light raw video enhancement. Nevertheless, there are two main issues with
binarizing video enhancement models. One is how to fuse the temporal
information to improve low-light denoising without complex modules. The other
is how to narrow the performance gap between binary convolutions with the full
precision ones. To address the first issue, we introduce a spatial-temporal
shift operation, which is easy-to-binarize and effective. The temporal shift
efficiently aggregates the features of neighbor frames and the spatial shift
handles the misalignment caused by the large motion in videos. For the second
issue, we present a distribution-aware binary convolution, which captures the
distribution characteristics of real-valued input and incorporates them into
plain binary convolutions to alleviate the degradation in performance.
Extensive quantitative and qualitative experiments have shown our
high-efficiency binarized low-light raw video enhancement method can attain a
promising performance.

最近，深度神经网络在低光原始视频增强方面取得了出色的性能。然而，它们通常具有高计算复杂性和大内存成本，限制了在资源有限设备上的应用。本文探讨了将极其紧凑的二值神经网络（BNN）应用于低光原始视频增强的可行性，并解决了融合时间信息和二值卷积之间的性能差距问题。经过广泛的定量和定性实验，我们的高效二值化低光原始视频增强方法取得了令人满意的性能。