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.

该研究通过引入新的数据合成流程来模拟真实的低照度模糊退化，提出了联合低照度增强和去模糊的大规模数据集 LOL-Blur，同时提出了一种名为 LEDNet 的有效网络，解决了夜间摄影低照度和模糊问题。

LEDNet: 低光弱化与去模糊联合处理

LEDNet: Joint Low-light Enhancement and Deblurring in the Dark

The extensive computational burden limits the usage of CNNs in mobile devices
for dense estimation tasks. In this paper, we present a lightweight network to
address this problem,namely LEDNet, which employs an asymmetric encoder-decoder
architecture for the task of real-time semantic segmentation.More specifically,
the encoder adopts a ResNet as backbone network, where two new operations,
channel split and shuffle, are utilized in each residual block to greatly
reduce computation cost while maintaining higher segmentation accuracy. On the
other hand, an attention pyramid network (APN) is employed in the decoder to
further lighten the entire network complexity. Our model has less than 1M
parameters,and is able to run at over 71 FPS in a single GTX 1080Ti GPU. The
comprehensive experiments demonstrate that our approach achieves
state-of-the-art results in terms of speed and accuracy trade-off on CityScapes
dataset.

本文介绍一种名为 LEDNet 的轻量级网络，采用了不对称的编码器 - 解码器架构和 ResNet 作为骨干网络，以及每个残差块中的两种新操作：通道分割和随机洗牌。在解码器中采用注意金字塔网络 (APN) 来进一步减轻整个网络的复杂性。实验证明，我们的模型在速度和准确性方面均取得了最好的结果，并在 CityScapes 数据集上实现了最先进的结果。