Attention based Broadly Self-guided Network for Low light Image Enhancement
This addresses faster and more effective low-light image enhancement for real-world applications, though it appears incremental as it builds on existing self-guided network approaches.
The authors tackled low-light image enhancement by proposing an Attention based Broadly Self-guided Network (ABSGN) to reduce inference time while extracting local and global features, achieving results that outperform most state-of-the-art solutions on mainstream benchmarks.
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.