During the past years,deep convolutional neural networks have achieved impressive success in low-light Image Enhancement.Existing deep learning methods mostly enhance the ability of feature extraction by stacking network structures and deepening the depth of the network.which causes more runtime cost on single image.In order to reduce inference time while fully extracting local features and global features.Inspired by SGN,we propose a Attention based Broadly self-guided network (ABSGN) for real world low-light image Enhancement.such a broadly strategy is able to handle the noise at different exposures.The proposed network is validated by many mainstream benchmark.Additional experimental results show that the proposed network outperforms most of state-of-the-art low-light image Enhancement solutions.
翻译:在过去几年里,深层的进化神经网络在低光图像增强方面取得了令人印象深刻的成功。 现有的深层学习方法主要通过堆叠网络结构和深化网络深度来提高地物提取能力。 这给单一图像带来更多的运行时间成本。 为了减少推论时间,同时充分提取当地特征和全球特征。 在SGN的启发下,我们提议为现实世界低光图像增强建立一个基于关注的宽广自导网络。 这种广泛的战略能够处理不同暴露点的噪音。 提议建立的网络得到许多主流基准的验证。 额外实验结果显示,拟议的网络比最先进的低光度图像增强解决方案效果更好。