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Super resolution from a single image

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Lakkhana Mallikarachchi
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Super-Resolution from a Single Image by Daniel Glasner, Shai Bagon and Michal Irani
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/
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/
 The need of Super Resolution  Main approaches of SR  Proposed method  Experimental results of the proposed method Outline
Why Super Resolution? Low Resolution High Resolution Resize
 Generating high resolution image with more resolving power using one or more low resolution images. › more resolving power – more details What Is Super Resolution?
 Multi image super resolution  Example based super resolution Super Resolution Methods
 Several images of the same scenery.  Each image will have different information of the same scenery. Multi Image Super Resolution
 Image database with HR/LR image pairs  Replace similar LR patches with corresponding HR patches. Example Based Super Resolution + LR HR
 Combine multi image SR with example based SR  Without use external source Proposed Approach
 5x5 pixel image patches  More than 60% of image patches have 9 or more recurrences within same scale or in different scales Patch Redundancy
 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
 Problem Model L1 L2 L3 H
 Problem Model p q
 Find similar patches within scale  Nearest Neighbor Multi Image to Single Image
How to find cross-scale patch redundancy?
Finding Similar Patches I0 = L I-1 I1 = H
Finding Similar Patches I0 = L I-1 I1 I2 = H
 Finding Similar Patches FindNN I0 = L I-1 I-2 I1 I2 = H
 Finding Similar Patches Parent FindNN Parent I0 = L I-1 I-2 I1 I2 = H
 Finding Similar Patches Parent FindNN Parent Copy I0 = L I-1 I-2 I1 I2 = H
 RGB YIQ  Extract Y component (Luminance)  Apply SR to Y component  Use interpolation methods to I and Q components (Chrominance)  Combine YIQ Color Images
Experimental Results Bi-cubic interpolation Proposed Method LR
Experimental Results Nearest NeighborProposed Method LR
Experimental Results Example BasedProposed Method LR
Experimental Results Bi-cubic interpolation Proposed Method LR
 Two main approaches of Super Resolution  Observation about patch redundancy  Unified approach of Super Resolution  Experimental results Summery
 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
Any Question?
Thank you…

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Super resolution from a single image

Editor's Notes

  1. In the previous case when we using interpolation methods they have no more resolving power than the LR image.How can we have more details than the LR image.
  2. Differencese in angleTime diff
  3. not guaranteed to provide the true (unknown) high resolution details.
  4. Statistically analyzed a large image database to find the redundancy of image patches within the same image
  5. Statistically analyzed a large image database to find the redundancy of image patches within the same image
  6. Statistically analyzed a large image database to find the redundancy of image patches within the same image
  7. Nearest neighbor algorithm
  8. We don’t know the pair