Exampled-based Super resolution

1. Exampled-based Super resolution Presenter: Yu-Wei Fan

2. Outline • Introduction • Training set generation • Super-resolution algorithms • Idea • Markov Network • One-pass algorithm • Results

3. Outline • Introduction • Training set generation • Super-resolution algorithms • Idea • Markov Network • One-pass algorithm • Results

4. Introduction • Why do we need high resolution image? • Usually , we cannot get high resolution image easy.

5. Introduction • Aim: High Resolution Image • 1.Reduce the pixel size • the amount of light available also decrease • generates shot noise • 2.Increase the chip size • increase capacitance • difficult to speed up a charge transfer rate • 3.Signal processing techniques • Low cost

6. Introduction • General Super Resolution • Need multi frames information • Exampled-based Super resolution • Need only one frame

7. Outline • Introduction • Training set generation • Super-resolution algorithms • Idea • Markov Network • One-pass algorithm • Results

8. Training set generation • Store the high-resolution patch corresponding to every possible • low-resolution image patch. • Typically, these patches are 5 × 5 or 7 × 7 pixels.

9. Outline • Introduction • Training set generation • Super-resolution algorithms • Idea • Markov Network • One-pass algorithm • Results

10. Idea Unfortunately, that approach doesn’t work!

11. Markov Network

12. Markov Network • MAP Estimator:

13. Markov Network • Example:

14. Markov Network • Belief Propagation Where is from the previous iteration. The initial are 1. Typically, three or four iterations of the algorithm are sufficient.

15. One-pass algorithm • How do we select a good patch pair? • Two constraint: • frequency constraint • spatial constraint

16. One-pass algorithm

17. Outline • Introduction • Training set generation • Super-resolution algorithms • Idea • Markov Network • One-pass algorithm • Results

18. Results

19. Results

20. Results • α=0

21. Results • α=0.5

22. Results • α=5