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Attention based Broadly Self-guided Network for Low light Image Enhancement

arXiv 2112.06226 ↗First posted 12 Dec 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 12 Dec 2021.

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23 of 50 references.

References · 23

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11 more
  1. 2017Deep Bilateral Learning for Real-Time Image Enhancementcited by 200
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  9. 2020Deep Bilateral Retinex for Low-Light Image Enhancementcited by 5
  10. 2020GIA-Net: Global Information Aware Network for Low-light Imagingcited by 5
  11. 2020MIRNet: Learning multiple identities representations in overlapped speechcited by 3

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