去模糊
计算机科学
管道(软件)
接头(建筑物)
人工智能
能见度
计算机视觉
计算摄影
图像(数学)
图像复原
图像处理
光学
物理
工程类
建筑工程
程序设计语言
作者
Shangchen Zhou,Chongyi Li,Chen Change Loy
标识
DOI:10.1007/978-3-031-20068-7_33
摘要
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 sharpness. 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, especially for blurs in saturated regions, e.g., light streaks, that often appear in the night images. 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 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.
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