A convolutional neural network based method for low-illumination image enhancement-Reference-Cited by-同舟云学术

A convolutional neural network based method for low-illumination image enhancement

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition - AIPR '19
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Author:

Huang Huang¹,Tao Haijun¹,Wang Haifeng²

Affiliation:

1. China Jiliang University, Hangzhou, CHN

2. Beijing Institute of New Technology Applications, Beijing, CHN

Publisher

ACM Press

Reference22 articles.

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2. Girshick, R., Donahue, J., Darrell, T., and Malik, J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 580--587.

3. Jain, V., and Seung, S. 2009. Natural image denoising with convolutional networks. In Advances in neural information processing systems. 769--776.

4. Li, H., and Wu, X. J. 2019. DenseFuse: A Fusion Approach to Infrared and Visible Images. IEEE Transactions on Image Processing, 28(5), 2614--2623.

5. Ronneberger, O., Fischer, P., and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. 234--24. Springer, Cham.

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