用于弱光增强的夜间室外数据集

Yudong Zhou, Ronggang Wang, Yangshen Zhao
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引用次数: 0

摘要

弱光增强是近年来研究的热点,许多基于深度神经网络(DNN)的方法都取得了显著的效果。然而,深度神经网络的快速发展也对高质量的训练集提出了迫切的要求,特别是有监督的夜间数据集。在本文中,我们建立了一个包含1214组图像的夜间户外数据集(NOD 1)。我们还基于多曝光融合策略为每组生成合适的高质量参考图像,该策略不仅关注暗区,还提供了低光图像中过度曝光区域的细节。在此基础上,提出了一个简单有效的网络作为NOD的基线。在NOD和其他数据集上的实验结果表明了所提出的数据集和基线模型的泛化性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A night-time outdoor data set for low-light enhancement
Low light Enhancement has been a hot topic in recent years, and many deep neural network (DNN)-based methods have achieved remarkable performance. However, the rapid development of DNNs also raises the urgent requirement of high-quality training sets, especially supervised night-time data sets. In this paper, we establish a night-time outdoor data set (NOD 1) that contains 1214 groups of images. We also generate appropriate and high-quality reference images for each group based on multi-exposure fusion strategy, which not only focuses on dark areas but also provides details for over-exposed areas in low light images. Furthermore, a simple but efficient network is presented as the baseline of NOD. Experimental results on NOD and other data sets show the generalizability and effectiveness of the proposed data set and baseline model.
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