University of Groningen Institute of Mathematics and Computing Science
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Transcript of University of Groningen Institute of Mathematics and Computing Science
University of Groningen Institute of Mathematics and Computing Science
Universitá degli Studi di Roma TreDipartimento di Elettronica Applicata
Well Posed non-IterativeWell Posed non-Iterative
Edge and Corner Preserving SmoothingEdge and Corner Preserving Smoothing
For Artistic ImagingFor Artistic Imaging
Giuseppe Pápari, Nicolai Petkov, Patrizio Campisi
Photographical image
Output of the proposed operator
Smoothing out texture while preserving edges
Input image Gaussian smoothing Proposed operator
CoContentsntents•Kuwahara Filter and Generalizations•Limitations•Proposed Operator•Results and Comparison•Discussion
Kuwahara Filter and GeneralizationsKuwahara Filter and Generalizations
•Four local averages: ,
1, , d di
iQ x y
m x y IQ
22
,
1, , , d di
i iQ x y
s x y I m x yQ
•Four local standard deviations:
Kuwahara outputFor each pixel, value of mi that corresponds to the minimum standard deviation
Q1 Q2
Q3 Q4 2a
x
y
Generic pixel of the input image
Kuwahara Filter and GeneralizationsKuwahara Filter and Generalizations
Edge
Only the most homogeneous region is taken into account.
No smoothing across the edge
(x,y) = 1
Central pixel on the white side of the edge
(x,y) = 0
Central pixel on the black side of the edge
Kuwahara Filter and GeneralizationsKuwahara Filter and Generalizations
• Local averaging Smoothing
• Flipping due to Minimum Variance Criterion Edge Preserving
Kuwahara Filter and GeneralizationsKuwahara Filter and GeneralizationsAn example
Input image
Kuwahara output
Artifactson texture
Kuwahara Filter and GeneralizationsKuwahara Filter and GeneralizationsGeneralizations
•Number and shape of the sub-regions
»Pentagons, hexagons, circles»Overlapping
•Weighted local averages (reducing the Gibbs phenomenon)
»Gaussian-Kuwahara
• New class of filters (Value and criterion filter structure)
»N local averages and local standard deviations (computed as convolutions)
2 2 2,i i i i im I w s I m w
»Criterion: minimum standard deviation
•Connections with the PDEs theory and morphological analysis
•Kuwahara Filter and Generalizations
•LimitationsLimitations•Proposed Operator
•Results and Comparison
•Discussion
LimitationsLimitations• Artifacts
(partially eliminable with weighted averages)
• Not mathematically well defined
Equal standard deviations si • Devastating instability in presence of noise
LimitationsLimitationsSimple one-dimensional example
Input signal I(t)•I(t) =
kt
Local averagesI
ttT t+T
21 d
t
t T
k ktT
kT
Negative offset
• 1D Kuwahara filtering Two sub-windows w1 and w2 t
w2
t*
I(t)
w1
LimitationsLimitationsSimple one-dimensional example
Input signal I(t)•I(t) =
kt
Local averagesI
ttT t+T
21 d
t
t T
k ktT
kT
Negative offset
21 dt T
t
kt Tk kT
Positive offset
• 1D Kuwahara filtering Two sub-windows w1 and w2 t
w2
t*
I(t)
w1
•I(t) = kt
Local standard deviations
2 2
1 2 4k Ts t s t
•Equal standard deviations
I
ttT t+T
LimitationsLimitationsSimple one-dimensional example
Local averages m1(t), m2(t)
Input signal I(t) Local std. dev. s1(t), s2(t)
• 1D Kuwahara filtering Two sub-windows w1 and w2 t
w2
t*
I(t)
w1
LimitationsLimitations
Input image Kuwahara filtering Proposed approach
Synthetic two-dimensional example
Kuwahara
LimitationsLimitationsNatural image example
Input image Gauss-Kuwahara
Shadowed area
Depleted edge
Our approach
LimitationsLimitations
Ill-posedness of the minimum variance criterion.
Devastating effects in presence of noisy shadowed areas.
We propose
• Different weighting windows wi
• A different selection criterion instead of the minimum standard deviation
•Kuwahara Filter and Generalizations
•Limitations
•Proposed OperatorProposed Operator•Results and Comparison
•Discussion
Proposed OperatorProposed Operator
• Gaussian mask divided in N sectors N weighting windows
• N local averages and local standard deviations computed as convolutions
2 2 2,i i i i im I w s I w m
Weighting windows
2 2
222
1, ,2
x y
iiw x y g x y e
Proposed OperatorProposed OperatorSelection criterion
• q Only the minimum si survives Criterion and value
• Output:
ii
qi
qi
is
ms
» Weighted average of mi
» Weights equal to proportional to (si)q (q is a parameter)
Normalization
• High variance small coefficient (si)q
No undetermination in case of equal standard deviations!
Proposed OperatorProposed OperatorParticular cases
• Equal standard deviations: s1 = s2 = … = sN
1i
im
N
Gaussian smoothing
I g
• One standard deviation is equal to zero: sk = 0
km
• Several values of si are equal to zero
= Arithmetic mean of the corresponding values of mi.
Proposed OperatorProposed Operator
• EdgeHalf of the sectors have si = 0. The other ones are not considered
An example
• Edgeless areas:
All std. dev. similar Gaussian smoothing (no Gibbs phenomenon)
• Corner preservation
Automatic selection of the prominent sectors
Proposed OperatorProposed OperatorColor images
• 3 sets of local averages and local standard deviations, one for each color component
with
qi i
iqi
i
s
s
m
• Same combination rule
3 21 2 3
1, , ,
T ci i i i i i
cm m m s s
m
Not equivalentto apply the operator
to each color component separately
Proposed OperatorProposed OperatorIndependence on the color space
Input image RGB YCrCb L*a*b*
Proposed OperatorProposed OperatorWhy independence?
qi i
iqi
i
s
s
m
Linear transform. independentNonlinear transf. almost independent for homogeneous regions
• Local averages
Proposed OperatorProposed OperatorWhy independence?
Linear transform. independentNonlinear transf. almost independent for homogeneous regions
qi i
iqi
i
s
s
m
• Local averages
Low for homogeneous regions.The degree of homogeneity of a region does not depend on the color space.
• Local standard deviations
•Kuwahara Filter and Generalizations
•Limitations
•Proposed Operator
•Results and ComparisonResults and Comparison•Discussion
Results and comparisonResults and comparisonExisting algorithm for comparison
• Kuwahara filter and generalizations
• Bilateral filtering
• Morphological filters
• Median filters
Input image
Proposed approach
Gauss-Kuwahara filter
Input image (blurred)
Proposed approach (deblurred)
Bilateral filtering (not deblurred)
Input image
Proposed approach
Morphological closing (Struct. elem.: Disk of radius 5px)
Morphological area open-closing
Input image
Morphological area open-closing
Proposed approach
Input image
Proposed approach
Kuwahara Filter
Morphological area open-closing
Input image
Proposed approach
Bilateral Filtering
Input image
proposed aproach
55 median filter
Results and comparisonResults and comparisonLarger set of results and Matlab implementation available at
http://www.cs.rug.nl/~imaging/artisticsmoothing
• Graphical interface
•Kuwahara Filter and Generalizations
•Limitations
•Proposed Operator
•Results and Comparison
•DiscussionDiscussion
DiscussionDiscussion• Edge/corner preserving smoothing
• Undetermination for equal standard deviation
» Instability in presence of noise
» Discontinuities in presence of shadowed areas
• Criterion and value filter structure» Local averaging Smoothing
» Minimum variance criterion Edge preserving
DiscussionDiscussion• Proposed approach
» Different windows
» Different criterion
Mathematically well defined operator
Adaptive choice of the most appropriate sub-regions.
Our approachGauss-KuwaharaKuwahara
DiscussionDiscussion• Limitations
» Lines are thinned
» Small objects are not preserved
ReferencesReferences•G. Papari, N. Petkov, P. CampisiArtistic Edge and Corned Preserving SmoothingTo appear on IEEE Transactions on Image Processing, 2007
•G. Papari, N. Petkov, P. CampisiEdge and Corned Preserving Smoothing for Artistic ImagingProceedings SPIE 2007 Image Processing: Algorithms and Systems, San Jose, CA