1 code implementation • 12 Dec 2021 • Zilong Chen, Yaling Liang, Minghui Du
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.
no code implementations • 29 Jan 2021 • Minghui Du, Lixin Xu
Besides, adopting tighter prior and employing multiple detectors both decrease the error of luminosity distance.
Cosmology and Nongalactic Astrophysics