Ruoyou Wu, Dexing Wang, Hongchun Yuan. Low-Light Image Enhancement Based on Attention Mechanism and Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201022
- Laser & Optoelectronics Progress
- Vol. 57, Issue 20, 201022 (2020)
Fig. 1. A-Unet network structure
Fig. 2. Algorithm flowchart
Fig. 3. Examples of sample data
Fig. 4. Qualitative comparison of synthetic low-light images obtained by different algorithms. (a) Image “flowersonih35”; (b) image “plane”; (c) image “house”; (d) image “lighthouse”
Fig. 5. Qualitative comparison of different algorithms on real low-light images. (a)(c) Images from DICM dataset; (b) image from LIME dataset; (d) image from MEF dataset
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Table 1. Performance comparison of different loss functions
|
Table 2. Quantitative comparison of synthetic low-light images obtained by different algorithms
|
Table 3. Quantitative comparison of different algorithms on real low-light images
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20