GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors-Reference-Cited by-同舟云学术

GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

He Jingwen¹,Shi Wu²,Chen Kai¹,Fu Lean¹,Dong Chao²

Affiliation:

1. ByteDance Inc

2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences,ShenZhen Key Lab of Computer Vision and Pattern Recognition, SIAT-SenseTime Joint Lab

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878551.pdf?arnumber=9878551

Reference43 articles.

1. Super-Identity Convolutional Neural Network for Face Hallucination

2. Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

3. Esrgan: En-hanced super-resolution generative adversarial networks;wang;Proceedings of the European Conference on Computer Vision (ECCV) Workshops,2018

4. Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform

5. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

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