1. Video Enhancement with Super-resolution 694410100 陳彥雄

2. Outline Introduction Bayesian MAP-based SR Exampled-based SR Ending

3. Introduction What is Super-Resolution (SR)? SR is an image-processing technology that enhance the resolution of an image system. SR fuses several low-resolution (LR) images together into one enhanced- resolution image.

4. SR vs. Interpolation & Filter Traditional interpolation methods, like bilinear, cubic splines, are applied to a single picture. But they add no additional information to high-frequency ranges. We can use filters sharpening up image details, but they also amplify noise. SR combines information form multiple sources.

5. SR in Video We can divide Video into several groups of picture, each GOP contains lots of similar content with block (object) motion. Since GOP contains lots of similar content, SR enhancement is achievable.

6. SR example From Wikipedia:

7. SR example Following videos source from: http://www.wisdom.weizmann.ac.il/~vision/Video Analysis/Demos/SpaceTimeSR/SuperRes_demo s.html http://www.wisdom.weizmann.ac.il/~vision/Video Analysis/Demos/SpaceTimeSR/SuperRes_demo s.html

8. SR example

10. Bayesian MAP-based SR

11. Maximum a Posteriori The following MAP example sources from: >

12. Maximum a Posteriori You have a bag of candy, which is one of follows: H1: 100%cherry H2: 75%cherry + 25%lime H3: 50%cherry + 50%lime H4: 25%cherry + 75%lime H5: 100%lime Which bag is at most possible if you get 2 lime candy from it? And what if the possibility of each bag is {H1,H2,H3,H4,H5} = {0.1, 0.2, 0.4, 0.2, 0.1}

13. Maximum a Posteriori

14. Bayesian MAP SR Notation

15. The Video Observation Model

16. Bayesian MAP SR Notation

17. Bayesian Maximum a posteriori

18. Exampled-based SR

19. Also called single-frame super resolution. Use data learning technology. It contains a training phase. Effectiveness depend by data.

20. Basic Idea Image can be decomposed by frequency into low, median and high. The low part of an Image is independent from the high ones. When an image is up scaled, it loses high frequency information. We can patch the high part of an upscale image from trained patch dictionary.

21. Training

22. SR Algorithm

25. SR result

27. Ending

28. Further Discussion Motion Vector matters! Image sequences or compressed video? Video on example-based SR? Other SR method?