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Edge-preserving Multiscale Image Decomposition based on Local Extrema

This paper presents a method for decomposing images into fine, medium, and coarse pixels to enhance details. Detail is separated into texture and shading, preserving edges. The algorithm uses local extrema to extract detail at different scales. Applications include image equalization and illumination transfer. Comparisons show the method outperforms existing approaches. Acknowledgments to INRIA and HFIBMR.

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Edge-preserving Multiscale Image Decomposition based on Local Extrema

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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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