kartic subr cyril soler frédo durand
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Kartic Subr Cyril Soler Frédo Durand
Edge-preserving Multiscale Image Decomposition
based on Local Extrema
Edge-preserving Multiscale Image Decomposition
based on Local Extrema
INRIA, Grenoble Universities MIT CSAIL
Multiscale image decomposition
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Medium
Pixels
Intensity
Input
Fine
Coarse
1D
Motivation
Detail enhancement
Separating fine texturefrom coarse shading
What is detail?
Some examples
Related work
Linear multiscale methods Edge-preserving approaches
1D Signal analysis
Related work: Linear multiscale methods
Edge-preserving approaches
1D Signal analysis
[Burt and Adelson 93]
[Rahman and Woodell 97]
[Pattanaik et al 98]
[Lindeberg 94]
Edges not preserved(Causes halos while editing)
Related work: Edge-preserving methods
1D Signal analysis
[Farbman et al 08] [Fattal et al 07]
[Bae et al 07] [Chen et al 07]
Edge-aware
Assume detail is low contrast
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
Input
Base layer
Detail layer(Input – Base)
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Edge-preserving smoothing(e.g. bilateral filter)
Edge (preserved)
Detail (smoothed)
Existing edge-preserving image decompositions
Assume detail is low-intensity variation
Challenge: Smoothing high-contrast detail
Input
Challenge: Smoothing high-contrast detail
Edge
Low-contrast detail
High-contrast detail
Conservative smoothing (bilateral filter with narrow range-Gaussian)
Challenge: Smoothing high-contrast detail
Edge preserved?
Low-contrast detail smoothed?
High-contrast detail smoothed?
Challenge: Smoothing high-contrast detail
Edge preserved?
Low-contrast detail smoothed?
High-contrast detail smoothed?
Aggressive smoothing(bilateral filter with wide range-Gaussian)
Example: Smoothing high-contrast detail
Input [Farbman et al 2008] λ= 13, α = 0.2
[Farbman et al 2008] λ= 13, α = 1.2
Detail not smoothedDetail not smoothed
Coarse features smoothedEdge smoothed
Our approach: Use local extrema
Input
Local maxima
Local minima
Detail = oscillations between local extrema
Our approach: Use local extrema
Base = Local mean of neighboring extrema
Our approach: Use local extrema
Local mean of neighboring extrema
Edge preserved?
Low-contrast detail smoothed?
High-contrast detail smoothed?
Our detail extraction
Input
Base layer
Detail layer
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High-contrastdetail smoothed
Edges preserved
Algorithm
Identify local extrema
Estimate smoothed mean
Detail at multiple scales
Input: Image + number of layers
Algorithm: Illustrative example
Algorithm: Identifying local extrema
Extrema detection kernel
Local maxima
Local minima
Algorithm: Estimating smoothed mean
1) Construct envelopes
Minimal envelope Interpolation preserves edge[Levin et al 04]
Maximal envelope
Algorithm: Estimating smoothed mean
2) Average envelopes
Estimated mean
Algorithm: After one iteration
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Input
Base
Detail
Algorithm: Mean at coarser scale
Local maxima
Local minima
Widen extrema detection kernel
Algorithm: Mean at coarser scale
Minimal envelope
Maximal envelope
Algorithm: Mean at coarser scale
Estimated mean
Identify local extrema
Construct envelopes
Average envelopes
Recap: Detail extraction
Smoothed mean
Detail = Input - BaseBase
Input
Base B2
Base B1
Input
Detail D2
Detail D1
Recap: Multiscale decomposition
Layer 1Layer 2Layer 3 Iteration 1on input
Iteration 2on B1
Recurse n-1 times for n-layers
Coarse Fine
Results
Results: Smoothing
Input
Smoothed
Results: Multiscale decomposition
Medium
Input
FineCoarse
Low contrast edge High contrast detailLow contrast edge High contrast detail
Results: Multiscale decomposition
Input
Results: Multiscale decomposition
FineCoarse
Applications: Image equalization
Applications: Smoothing hatched images
Applications: Coarse illumination transfer
Applications: Coarse illumination transfer
Applications: Coarse illumination transfer
Applications: Tone-mapping HDR images
Comparison
[Farbman et al 2008]Our Result
Our smoothing
Limitation
Input Our Result
Conclusion
Detail based on local extrema
Smoothing high contrast detail
Edge-preserving multiscale decomposition
Acknowledgements
INRIA post-doctoral fellowship
Equipe Associée with MIT ‘Flexible Rendering’
Adrien Bousseau & Alexandrina Orzan
HFIBMR grant (ANR-07-BLAN-0331)
Anonymous reviewers
C++ source: http://artis.imag.fr/~Kartic.Subr/research.html
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