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Kartic Subr Cyril Soler Frédo Durand

Edge-preserving Multiscale Image Decomposition based on Local Extrema. Kartic Subr Cyril Soler Frédo Durand. MIT CSAIL. INRIA, Grenoble Universities. Multiscale image decomposition. 1D. Intensity. Input. Fine. +. Medium. +. Coarse. Pixels. Motivation.

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Kartic Subr Cyril Soler Frédo Durand

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  1. Edge-preserving Multiscale Image Decomposition based on Local Extrema Kartic Subr Cyril Soler Frédo Durand MIT CSAIL INRIA, Grenoble Universities

  2. Multiscale image decomposition 1D Intensity Input Fine + Medium + Coarse Pixels

  3. Motivation Detail enhancement Separating fine texture from coarse shading

  4. What is detail?

  5. Some examples

  6. Related work Linear multiscale methods Edge-preserving approaches 1D Signal analysis

  7. Related work: Linear multiscale methods Edge-preserving approaches [Lindeberg 94] [Burt and Adelson 93] Edges not preserved 1D Signal analysis (Causes halos while editing) [Rahman and Woodell 97] [Pattanaik et al 98]

  8. Related work: Edge-preserving methods [Fattal et al 07] [Farbman et al 08] Edge-aware Assume detail is low contrast 1D Signal analysis [Chen et al 07] [Bae et al 07]

  9. Related work: Empirical mode decomposition Linear multiscale Edge-preserving approaches [Huang et al 98] Developed for 1D signals Detail depends on spatial scale Not edge-aware

  10. Existing edge-preserving image decompositions Edge (preserved) Input Detail (smoothed) Assume detail is low-intensity variation Edge-preserving smoothing (e.g. bilateral filter) Base layer + Detail layer (Input – Base)

  11. Challenge: Smoothing high-contrast detail Input

  12. Challenge: Smoothing high-contrast detail High-contrast detail Edge Low-contrast detail

  13. Challenge: Smoothing high-contrast detail High-contrast detail smoothed? Edge preserved? Low-contrast detail smoothed? Conservative smoothing (bilateral filter with narrow range-Gaussian)

  14. Challenge: Smoothing high-contrast detail High-contrast detail smoothed? Edge preserved? Low-contrast detail smoothed? Aggressive smoothing (bilateral filter with wide range-Gaussian)

  15. Example: Smoothing high-contrast detail Detail not smoothed Detail not smoothed Edge smoothed Coarse features smoothed Input [Farbman et al 2008] λ= 13, α= 0.2 [Farbman et al 2008] λ= 13, α= 1.2

  16. Our approach: Use local extrema Detail = oscillations between local extrema Local maxima Input Local minima

  17. Our approach: Use local extrema Base = Local mean of neighboring extrema

  18. Our approach: Use local extrema High-contrast detail smoothed? Edge preserved? Low-contrast detail smoothed? Local mean of neighboring extrema

  19. Our detail extraction Input High-contrast detail smoothed Base layer + Edges preserved Detail layer

  20. Algorithm Input: Image + number of layers Identify local extrema Estimate smoothed mean Detail at multiple scales

  21. Algorithm: Illustrative example

  22. Algorithm: Identifying local extrema Extrema detection kernel Local maxima Local minima

  23. Algorithm: Estimating smoothed mean 1) Construct envelopes Maximal envelope Minimal envelope Interpolation preserves edge [Levin et al 04]

  24. Algorithm: Estimating smoothed mean 2) Average envelopes Estimated mean

  25. Algorithm: After one iteration Input Base + Detail

  26. Algorithm: Mean at coarser scale Widen extrema detection kernel Local maxima Local minima

  27. Algorithm: Mean at coarser scale Maximal envelope Minimal envelope

  28. Algorithm: Mean at coarser scale Estimated mean

  29. Recap: Detail extraction Input Identify local extrema Construct envelopes Base Detail = Input - Base Average envelopes Smoothed mean

  30. Recap: Multiscale decomposition Iteration 1 on input Layer 3 Layer 2 Layer 1 Input Coarse Fine Iteration 2 on B1 Base B1 Detail D1 Recurse n-1 times for n-layers Detail D2 Base B2

  31. Results

  32. Results: Smoothing Input Smoothed

  33. Results: Multiscale decomposition Input Low contrast edge Low contrast edge High contrast detail High contrast detail Coarse Medium Fine

  34. Results: Multiscale decomposition Input

  35. Results: Multiscale decomposition Fine Coarse

  36. Applications: Image equalization

  37. Applications: Smoothing hatched images

  38. Applications: Coarse illumination transfer

  39. Applications: Coarse illumination transfer

  40. Applications: Coarse illumination transfer

  41. Applications: Tone-mapping HDR images

  42. Comparison Our Result [Farbman et al 2008]

  43. Our smoothing

  44. Limitation Input Our Result

  45. Conclusion Detail based on local extrema Smoothing high contrast detail Edge-preserving multiscale decomposition

  46. Acknowledgements INRIA post-doctoral fellowship HFIBMR grant(ANR-07-BLAN-0331) Equipe Associée withMIT ‘Flexible Rendering’ Adrien Bousseau & Alexandrina Orzan Anonymous reviewers

  47. C++ source: http://artis.imag.fr/~Kartic.Subr/research.html

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