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  • 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.
  • Differencese in angleTime diff
  • not guaranteed to provide the true (unknown) high resolution details.
  • Statistically analyzed a large image database to find the redundancy of image patches within the same image
  • Statistically analyzed a large image database to find the redundancy of image patches within the same image
  • Statistically analyzed a large image database to find the redundancy of image patches within the same image
  • Nearest neighbor algorithm
  • We don’t know the pair


  • 1. Super-Resolution from aSingle Imageby 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 ofScience, 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 WeizmannInstitute of Science, Israel.› Research Interests - Computer Vision and VideoInformation 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 proposedmethodOutline
  • 5. Why Super Resolution?Low ResolutionHigh ResolutionResize
  • 6.  Generating high resolution imagewith more resolving powerusing one or more low resolutionimages.› more resolving power – more detailsWhat Is Super Resolution?
  • 7.  Multi image super resolution Example based super resolutionSuper Resolution Methods
  • 8.  Several images of the same scenery. Each image will have differentinformation of the same scenery.Multi Image Super Resolution
  • 9.  Image database with HR/LR image pairs Replace similar LR patches withcorresponding HR patches.Example Based Super Resolution+LR HR
  • 10.  Combine multi image SR with examplebased SR Without use external sourceProposed Approach
  • 11.  5x5 pixel image patches More than 60% of image patches have 9or more recurrences within same scale orin different scalesPatch Redundancy
  • 12.  Use patch redundancy in same scale tomodel multi image super resolutionproblem Use patch redundancy in different scalesto model example based superresolution problemProposed Method
  • 13. Problem ModelL1 L2 L3H
  • 14. Problem Modelpq
  • 15.  Find similar patches within scale Nearest NeighborMulti Image to Single Image
  • 16. How to find cross-scale patchredundancy?
  • 17. Finding Similar PatchesI0 = LI-1I1 = H
  • 18. Finding Similar PatchesI0 = LI-1I1I2 = H
  • 19. Finding Similar PatchesFindNNI0 = LI-1I-2I1I2 = H
  • 20. Finding Similar PatchesParentFindNN ParentI0 = LI-1I-2I1I2 = H
  • 21. Finding Similar PatchesParentFindNN Parent CopyI0 = LI-1I-2I1I2 = H
  • 22.  RGB YIQ Extract Y component (Luminance) Apply SR to Y component Use interpolation methods to I and Qcomponents (Chrominance) Combine YIQColor Images
  • 23. Experimental ResultsBi-cubic interpolation Proposed MethodLR
  • 24. Experimental ResultsNearest NeighborProposed MethodLR
  • 25. Experimental ResultsExample BasedProposed MethodLR
  • 26. Experimental ResultsBi-cubic interpolation Proposed MethodLR
  • 27.  Two main approaches of SuperResolution Observation about patch redundancy Unified approach of Super Resolution Experimental resultsSummery
  • 28.  D. Glasner, S. Bagon and M. Irani, "Super-resolution from a singleimage," 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/pachecojReferences
  • 29. Any Question?
  • 30. Thank you…