  Night photography typically suffers from both low light and blurring issues
due to the dim environment and the common use of long exposure. While existing
light enhancement and deblurring methods could deal with each problem
individually, a cascade of such methods cannot work harmoniously to cope well
with joint degradation of visibility and textures. Training an end-to-end
network is also infeasible as no paired data is available to characterize the
coexistence of low light and blurs. We address the problem by introducing a
novel data synthesis pipeline that models realistic low-light blurring
degradations. With the pipeline, we present the first large-scale dataset for
joint low-light enhancement and deblurring. The dataset, LOL-Blur, contains
12,000 low-blur/normal-sharp pairs with diverse darkness and motion blurs in
different scenarios. We further present an effective network, named LEDNet, to
perform joint low-light enhancement and deblurring. Our network is unique as it
is specially designed to consider the synergy between the two inter-connected
tasks. Both the proposed dataset and network provide a foundation for this
challenging joint task. Extensive experiments demonstrate the effectiveness of
our method on both synthetic and real-world datasets.
