definition of anisotropic denoising operators via sectional curvature stanley durrleman
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Definition of anisotropic denoising operators via sectional curvature Stanley Durrleman. September 19, 2006. The problem. crest. coast. Purpose : Denoising homogeneous areas… …without smoothing the signal at the interfaces. The problem. Autoregressive model :. The problem. - PowerPoint PPT PresentationTRANSCRIPT
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Air Systems Division
Definition of anisotropic denoising operators viasectional curvature
Stanley DurrlemanSeptember 19, 2006
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The problem
coast
crest
Purpose : Denoising homogeneous areas… …without smoothing the signal at the interfaces
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The problem
Autoregressive model :
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The problem
Autoregressive model :
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The problem
Autoregressive model :
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The problem
Autoregressive model :
Burg algorithm enables :-better estimation in case of short sample signals-fewer interference peaks-recursive computation : real time algorithm-estimation of the spectral density function :
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The problem
Example : record of turbulent atmospheric clutter Images du CR 1magnitude angle
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What’s in the image proceesing toolbox ?
- Statistical models of noiseBayesian models, Markov fields… :
- good model of noise- how to take the geometry into account ?
- Geometrical models : Linear filters (Gaussian,…) : do not preserve the discontinuitiesNon-linear filters :- Curvature motion & morphologic filters (AMSS, mean curvature motion, median filter) :
- noise = level set of small areas- specific for gray-level images
- Geometric filters : (Kimmel, Sochen, Barbaresco) : - model data as a sub-manifold- depend on the way data are parametrized (mean curvature flow)- model of noise ?
Our goal : define anisotropic operators that can denoise data… of any dimension (gray-level images, radar signal…) independently of the data parametrization and restore piecewise constant data
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Outline
Noise characterization via sectional curvature
De-noising algorithms
Results
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I. Noise characterization through sectional curvature
MIA – September 19, 2006
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I. Noise & Sectional Curvature
1. Question : what is noise ??
statistics : Bayesian filters, maximum likelihood…
geometry : which tool ? Gradient ? Curvature !
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I. Noise & Sectional Curvature
2- Basic idea : the surface Gaussian Curvature
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I. Noise & Sectional Curvature
2- Basic idea : the surface Gaussian Curvature
Examples :
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I. Noise & Sectional Curvature
Noise and curvatureAxiom : pixel of noise = pixel of big curvatureAxiom : pixel of noise = pixel of big curvature
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How to denoise ?
By minimizing the following energy :
I. Noise & Sectional Curvature
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I. Noise & Sectional Curvature
3 – Modeling
A generic ‘image’ :
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I. Noise & Sectional Curvature
3 – Modeling
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I. Noise & Sectional Curvature
3 – ModelingCurvature of a metric :
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I. Noise & Sectional Curvature
3 – ModelisationCurvature of a metric :
That is the surface Gaussian curvature !That is the surface Gaussian curvature !
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I. Noise & Sectional Curvature
Summary :1/ One defines :
h metric on the data space e metric on the acquisition space
=> a ‘mixed’ metric : g
2/ One computes the sectional curvature: K
3/ One defines the energy : E
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Air Systems Division
II. De-noising algorithms
MIA’06 - September 19, 2006
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II. De-noising algorithms
Purpose :Minimizing :
2 methods :- Partial Differential Equation- Stochastic algorithm
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1. Descent gradient scheme :1/ initialise with the given noisy image2/ Evolve towards a minimum of :
using the gradient :
Hence, the evolution equation :
implemented with a finite difference scheme.
II. De-noising algorithms
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II. De-noising algorithms
Case of gray-level images :
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II. De-noising algorithms
2. Stochastic method :
- One picks randomly a pixel in the (noisy) image.
- One adds a small random Gaussian variable to the pixel’s value.
- If the energy decreases : one keeps the change Else : the change is rejected.
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III. Results
MIA’06 - September 19, 2006
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III. Results
1 – Gray-level images (1)
Metric :
Curvature :
Flow :
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III. Results
t = 0 t = 1 t = 10 t = 100
Flow equation
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III. Results
Transversal view :
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III. Results
Stochastic
Stoch. + PDE
Stochastic algorithm :Stochastic algorithm :
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III. Results
2 – Gray level images (2) Adaptative metric :
‘Dilate’ geodesics in D far from the minimum
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III. Results
original PDE Stoch. Algo. Adaptative metric
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III. Results
median
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III. Results
RSO Image
original amss PDE Stoch
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III. Results
2. Radar signal
Reminder : Parametrization thanks to complex auto-regressive analysiscomplex auto-regressive analysis
8 complex magnitudes
7 reflection coefficients
Doppler spectrum
Burg Algo.
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III. Results
Data : Reflection coefficients
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III. Results
Simulated data :Image of CR 1 :
magnitude angle
azimut 16
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III. Results
Simulated data :Image of CR 1 :
magnitude angle
azimut 16
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III. Results
After de-noising:
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III. Results
After de-noising :
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III. Results
Real data : Images du CR 1
magnitude angle
azimut 19
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III. Results
After de-noising :
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Air Systems Division
Thank you for your kind attention
MIA’06 - September 19, 2006
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