Download - 1 Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal 國立交通大學電子研究所 張瑞男 2008.12.11
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Outline
Introduction of Wavelet Transform Variance Stabilization Transform of a Filtered
Poisson Process (VST) Denoising by Multi-scale VST + Wavelets Ridgelets & Curvelets Conclusions
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Introduction of Wavelet Transform(10/18)
Multiresolution Analysis The spanned spaces are nested:
Wavelets span the differences between spaces wi.Wavelets and scaling functions should be orthogonal: simple calculation of coefficients.
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Introduction of Wavelet Transform(12/18)
Multiresolution Formulation.
( Scaling coefficients)
( Wavelet coefficients)
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Introduction of Wavelet Transform(13/18)
Discrete Wavelet Transform (DWT) Calculation: Using Multi-resolution Analysis:
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Introduction of Wavelet Transform(14/18)
Basic idea of Fast Wavelet Transform
(Mallat’s herringbone algorithm): Pyramid algorithm provides an efficient calculation.
DWT (direct and inverse) can be thought of as a filtering process.
After filtering, half of the samples can be eliminated: subsample the signal by two.
Subsampling: Scale is doubled. Filtering: Resolution is halved.
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Introduction of Wavelet Transform(15/18)
(a) A two-stage or two-scale FWT analysis bank and
(b) its frequency splitting characteristics.
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From http://www.cerm.unifi.it/EUcourse2001/Gunther_lecturenotes.pdf, p.10
Introduction of Wavelet Transform(18/18)
Comparison of Transformations
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VST of a Filtered Poisson Process(1/4)
Poisson process
Filtered Poisson process
assume
Seek a transformation
)(~: iiXX
i
ijj XihY ][
i
kk ih ])[(
)(: YTZ
1][ ZVar
ij
λ : intensity
1][][ i
ihYE 22])[(][
i
ihYVar
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VST of a Filtered Poisson Process(2/4)
Taylor expansion & approximation
i
ijj XihY ][ i
kk ih ])[( ij
Solution
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VST of a Filtered Poisson Process(3/4) Square-root transformation
Asymptotic property
Simplified asymptotic analysis
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Denoising by MS-VST + Wavelets(1/14)
Main steps
(1) Transformation (UWT)
(2) Detection by wavelet-domain hypothesis test
(3) Iterative reconstruction (final estimation)
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Denoising by MS-VST + Wavelets(5/14) Detection by wavelet-domain hypothesis test
(hard threshold)
p : false positive rate (FPR)
: standard normal cdf
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Denoising by MS-VST + Wavelets(6/14) Iterative reconstruction (soft threshold)
a constrained sparsity-promoting minimization problem
R: weak-generalized left inverse synthesis operatorW: wavelet transform operator
(positive projection)
(pseudo-inverse operator)
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Denoising by MS-VST + Wavelets(7/14) Iterative reconstruction
hybrid steepest descent (HSD)
+P : the projection onto the nonnegative orthant
: step sequence
mind C 2
1
( ) :L
i
J d d i
1 2:C S S
1 2:C S ST P Q
( 0 )
( ) * >0, d dk
k
unique solution
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lim 0, , , k k k kk
k k
Denoising by MS-VST + Wavelets(8/14)
Iterative reconstruction hybrid steepest descent (HSD)
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positive projection
significant coefficient
originalcoefficient
gradient component k
updatedcoefficient
kd
1SP
2SQ
J
1kd
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Denoising by MS-VST + Wavelets(11/14)
Applications and resultsSimulated Biological Image Restoration
oringinal image observed photon-count image
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Denoising by MS-VST + Wavelets(12/14) Applications and resultsSimulated Biological Image Restoration
denoised by Haar hypothesis tests MS-VST-denoised image
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Denoising by MS-VST + Wavelets(13/14) Applications and resultsAstronomical Image Restoration
Galaxy image observed image
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Denoising by MS-VST + Wavelets(14/14)
Applications and resultsAstronomical Image Restoration
denoised by Haar hypothesis tests MS-VST-denoised image
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Ridgelets & Curvelets (1/11) Ridgelet Transform (Candes, 1998):
Ridgelet function:
The function is constant along lines. Transverse to these ridges, it is a wavelet.
dxxfxbaR baf ,,,,
a
bxxaxba
)sin()cos( 212
1
,,
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Ridgelets & Curvelets (2/11)
The ridgelet coefficients of an object f are given by analysis of the Radon transform via:
dta
bttRAbaR ff )(),(),,(
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Ridgelets & Curvelets (4/11) Results of MS-VST With Ridgelets
Intensity Image Poisson Noise Image
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Ridgelets & Curvelets (5/11) Results of MS-VST With Ridgelets
Intensity Image Poisson Noise Image
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Ridgelets & Curvelets (6/11) Results of MS-VST With Ridgelets
denoised by MS-VST+UWT MS-VST + ridgelets
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Ridgelets & Curvelets (7/11)Curvelets Decomposition of
the original image into subbands
Spatial partitioning of each subband
Appling the ridgelet transform
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Ridgelets & Curvelets (10/11) Results of MS-VST With Curvelets Natural Image Restoration Intensity Image Poisson Noise Image
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Ridgelets & Curvelets (11/11) Results of MS-VST With Curvelets Natural Image Restoration denoised by MS-VST+UWT MS-VST + curvelets
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Conclusions It is efficient and sensitive in detecting faint features at a
very low-count rate. We have the choice to integrate the VST with the multiscale
transform we believe to be the most suitable for restoring a given kind of morphological feature (isotropic, line-like, curvilinear, etc).
The computation time is similar to that of a Gaussian denoising, which makes our denoising method capable of processing large data sets.