The document discusses unsupervised methods for image super-resolution, highlighting background concepts and various techniques such as interpolation-based and deep learning methods. It emphasizes the perception-distortion tradeoff and mentions CycleGANs as a key approach for learning image degradation and improving resolution. Furthermore, it references notable conferences and papers in the field, including works presented at CVPR and ICCV.

1. Unsupervised Methods for Image Super-Resolution

2. Introduction
Contents
-Background
-Unsupervised Methods for Image Super-Resolution
-Perception-Distortion Tradeoff

3. Background
Introduction

4. background
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Pixel prediction
Low resolution
High resolution
- Interpolation-based method
- (Deep) Learning-based method
Ex) bicubic

5. background
Collect Real Data
Advantage
Real data
No models required
Disadvantage
Specific setups
Cumbersome and expensive
Limitations

6. background
+ CycleGAN )
Adversarial loss
Cycle Consisitency loss
Full Objective

7. Unsupervised Methods for Image Super-Resolution
Unsupervised Methods for Image Super-Resolution

8. Unsupervised Methods for Image Super-Resolution
use a GAN to learn how to do image degradation
CVPR2018

9. Unsupervised Methods for Image Super-Resolution
Unsupervised Image Super-Resolution using Cycle-in-
Cycle Generative Adversarial Networks
CVPR2018

10. Unsupervised Methods for Image Super-Resolution
Unsupervised Image Super-Resolution using Cycle-in-
Cycle Generative Adversarial Networks
test
CVPR2018

11. Unsupervised Methods for Image Super-Resolution
Unsupervised Learning for Real-World Super-Resolution
ICCV2019

12. Unsupervised Methods for Image Super-Resolution
Unsupervised Learning for Real-World Super-Resolution
ICCV2019

13. Unsupervised Methods for Image Super-Resolution
Unsupervised Learning for Real-World Super-Resolution
test
ICCV2019

14. Unsupervised Methods for Image Super-Resolution
Cycle in cycle GAN

15. Unsupervised Methods for Image Super-Resolution
Paired Unpaired

16. The Perception-Distortion Tradeoff
Perception-Distortion Tradeoff

17. Perception-Distortion Tradeoff

18. Perception-Distortion Tradeoff
+ SRGAN )

19. Perception-Distortion Tradeoff
Perception-Distortion Tradeoff

20. Perception-Distortion Tradeoff
Perception-Distortion Tradeoff

22. references
The perception-distortion tradeoff, Yochai Blau, Tomer Michaeli; The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), 2018,
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks, Yuan
Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin; The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) Workshops, 2018,
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Christian Ledig,
Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan
Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi; The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2017
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks, Yuan
Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, Liang Lin; The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) Workshops, 2018
Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks, Jun-Yan Zhu,
Taesung Park, Phillip Isola, Alexei A. Efros; The IEEE International Conference on Computer Vision (ICCV),
2017,