Image Super-Resolution and Deblurring Using Generative Adversarial Network-Reference-Cited by-同舟云学术

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Image Super-Resolution and Deblurring Using Generative Adversarial Network

Published:2019-10-23 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition
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Author:

Du Bianli¹,Ren Xiaokang¹,Chen Saijian²,Ren Jie³,Cao Danling⁴

Affiliation:

1. College of Computer, Science&Engineering, Northwest Normal University

2. College of Computer and Information Engineering, Tianjin Normal University

3. School of Electronic and Information Engineering, Lanzhou City University

4. College of Computer Science&Engineering, Northwest Normal University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3373509.3373547

Reference27 articles.

1. Ledig C. Theis L. Husza´r F. Caballero J. Cunningham A. Acosta A. Aitken A. Tejani A. Totz J. Wang Z. etal: Photo-realistic single image super resolution using a generative adversarial network. In: CVPR.(2017) Ledig C. Theis L. Husza´r F. Caballero J. Cunningham A. Acosta A. Aitken A. Tejani A. Totz J. Wang Z. et al.: Photo-realistic single image super resolution using a generative adversarial network. In: CVPR.(2017)

2. DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

3. Regularized image up-sampling using a new observation model and the level set method

4. Image up-sampling using total-variation regularization with a new observation model

5. Example-based image super-resolution with class-specific predictors

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1. Deep architecture for super-resolution and deblurring of text images;Multimedia Tools and Applications;2023-05-20

2. Research and Application of GAN-GRU in Bridge Monitoring Data Cleaning;2022 China Automation Congress (CAC);2022-11-25

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