Binarized Low-light Raw Video Enhancement
Gengchen Zhang Yulun Zhang Xin Yuan Ying Fu
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.