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. 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

20. α=0

21. Results α=0.5

22. Results α=5