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Video Enhancement with Super-resolution

This video explores video enhancement using super-resolution (SR) techniques, detailing Bayesian MAP-based SR and example-based SR. We define super-resolution as a method that integrates multiple low-resolution images into a single high-resolution output, surpassing traditional interpolation methods. The effectiveness of SR in video is showcased, emphasizing its ability to utilize temporal similarities across frames for improved clarity. Viewers will gain insights into the underlying principles of SR, including algorithms and challenges in motion vector estimation. Join us to unravel the capabilities of modern image processing technologies.

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Video Enhancement with Super-resolution

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  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/VideoAnalysis/Demos/SpaceTimeSR/SuperRes_demos.html

  8. SR example

  9. SR example

  10. Bayesian MAP-based SR

  11. Maximum a Posteriori • The following MAP example sources from: <<Artificial Intelligence: A Modern Approach 2nd>>

  12. Maximum a Posteriori • You have a bag of candy, which is one of follows: • H1: 100%cherryH2: 75%cherry + 25%limeH3: 50%cherry + 50%limeH4: 25%cherry + 75%limeH5: 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. Exampled-based SR • 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

  23. SR Algorithm

  24. SR Algorithm

  25. SR result

  26. SR result

  27. Ending

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

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