1. Super-Resolution from a
Single Image
by Daniel Glasner, Shai Bagon and Michal Irani

2. Authors
 Daniel Glasner
› B.Sc and M.Sc at Tel Aviv University, Israel.
› Reading for a PhD at The Weizmann Institute of
Science, Israel.
› Web - http://www.wisdom.weizmann.ac.il/~glasner/
 Shai Bagon
› PhD at The Weizmann Institute of Science, Israel.
› Web - http://www.wisdom.weizmann.ac.il/~bagon/

3. Authors
 Michal Irani
› Professor in Computer Science at The Weizmann
Institute of Science, Israel.
› Research Interests - Computer Vision and Video
Information Analysis
› Web - http://www.wisdom.weizmann.ac.il/~irani/

4.  The need of Super Resolution
 Main approaches of SR
 Proposed method
 Experimental results of the proposed
method
Outline

5. Why Super Resolution?
Low Resolution
High Resolution
Resize

6.  Generating high resolution image
with more resolving power
using one or more low resolution
images.
› more resolving power – more details
What Is Super Resolution?

7.  Multi image super resolution
 Example based super resolution
Super Resolution Methods

8.  Several images of the same scenery.
 Each image will have different
information of the same scenery.
Multi Image Super Resolution

9.  Image database with HR/LR image pairs
 Replace similar LR patches with
corresponding HR patches.
Example Based Super Resolution
+
LR HR

10.  Combine multi image SR with example
based SR
 Without use external source
Proposed Approach

11.  5x5 pixel image patches
 More than 60% of image patches have 9
or more recurrences within same scale or
in different scales
Patch Redundancy

12.  Use patch redundancy in same scale to
model multi image super resolution
problem
 Use patch redundancy in different scales
to model example based super
resolution problem
Proposed Method

13. 
Problem Model
L1 L2 L3
H

14. 
Problem Model
p
q

15.  Find similar patches within scale
 Nearest Neighbor
Multi Image to Single Image

16. How to find cross-scale patch
redundancy?

17. Finding Similar Patches
I0 = L
I-1
I1 = H

18. Finding Similar Patches
I0 = L
I-1
I1
I2 = H

19. 
Finding Similar Patches
FindNN
I0 = L
I-1
I-2
I1
I2 = H

20. 
Finding Similar Patches
Parent
FindNN Parent
I0 = L
I-1
I-2
I1
I2 = H

21. 
Finding Similar Patches
Parent
FindNN Parent Copy
I0 = L
I-1
I-2
I1
I2 = H

22.  RGB YIQ
 Extract Y component (Luminance)
 Apply SR to Y component
 Use interpolation methods to I and Q
components (Chrominance)
 Combine YIQ
Color Images

23. Experimental Results
Bi-cubic interpolation Proposed Method
LR

24. Experimental Results
Nearest NeighborProposed Method
LR

25. Experimental Results
Example BasedProposed Method
LR

26. Experimental Results
Bi-cubic interpolation Proposed Method
LR

27.  Two main approaches of Super
Resolution
 Observation about patch redundancy
 Unified approach of Super Resolution
 Experimental results
Summery

28.  D. Glasner, S. Bagon and M. Irani, "Super-resolution from a single
image," in IEEE 12th International Conference on Computer Vision
(ICCV 2009), Kyoto, Japan, Sep. 29 - Oct. 2, 2009, pp. 349-356.
 http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html
 http://cs.brown.edu/courses/csci1950-g/results/final/pachecoj
References

29. Any Question?

30. Thank you…