PESiT: Progressive Joint Enhancement and Blind Super-Resolution for Low-Light and Low-Resolution Images Under Total Variation Constraints
Author:
Affiliation:
1. School of Computer Science and Technology, Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China
Funder
National Natural Science Foundation of China
14th Five Year Plan Hubei Provincial Advantaged Characteristic Disciplines Project of Wuhan University of Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10545558.pdf?arnumber=10545558
Reference41 articles.
1. Image super-resolution with an enhanced group convolutional neural network
2. MADNet: A Fast and Lightweight Network for Single-Image Super Resolution
3. Contrast-limited adaptive histogram equalization: speed and effectiveness
4. A low-light image enhancement method for both denoising and contrast enlarging
5. Dilated projection correction network based on autoencoder for hyperspectral image super-resolution
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