Multiscale Analysis of Photon-Limited Astronomical Images Rebecca Willett.
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Transcript of Multiscale Analysis of Photon-Limited Astronomical Images Rebecca Willett.
Multiscale Analysis of Photon-Limited Astronomical Images
Rebecca Willett
Photon-limited astronomical imaging
NG2997 Saturn
Richardson-Lucy performance on Saturn deblurring
Iteration Number
MSE of
deconvolved
estimate
Error performance of standard R-L
algorithm
Error performance of R-L algorithm with regularization
Main question: how to best perform Poisson intensity estimation?
Test data
Saturn Rosetta (Starck)
Methods reviewed in this talk
• Wavelet thresholding• Variance stabilizing transforms• Corrected Haar wavelet thresholds• Multiplicative Multiscale Innovation models– MAP estimation– EMC2 estimation– Complexity Regularization
• Platelets• á trous wavelet thresholding
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Wavelet thresholding
Sorted wavelet indexWavelet coefficient magnitude
Wavelet coefficients of Saturn
image
Approximation using wavelet coeffs. >
0.3
Saturn image
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Wavelet thresholding for denoising
Sorted wavelet indexNoise wavelet coefficient magnitude
Wavelet coefficients of Noisy
Saturn image
Estimate using wavelet coeffs. >
0.3
Noisy Saturn image
Translation invariance
1. Approximate with Haar wavelets as on previous slide
1. Shift image by 1/3 in each direction
2. approximate as before
3. shift back by 1/3Avoid this difficulty by averaging over all different possible shifts;
this can be done quickly with undecimated (redundant) wavelets
Wavelet thresholding resultsHaar
wavelets
Variance stabilizing transforms
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Anscombe 1948
Anscombe transform resultsHaar
wavelets
Kolaczyk’s corrected Haar thresholds
Kolaczyk 1999
Basic idea:Keep wavelet coeffs which correspond to signal;Threshold wavelet coeffs which correspond to noise (or background)
If we had Gaussian noise (variance 2) and no signal:
(j,k)th Gaussian wavelet coeff.
For Poisson noise, design similar bound for background 0 (noise):
(j,k)th Poisson wavelet coeff.
Threshold becomes:
Background intensity level
Corrected Haar threshold results
Multiplicative Multiscale Innovation Models (aka Bayesian Multiscale Models)
Timmermann & Nowak, 1999Kolaczyk, 1999
• Recursively subdivide image into squares• Let { denote the ratio between child and parent intensities
• Knowing {Knowing {• Estimate {} from empirical estimates based on counts in each partition square
0,0,0 X0,0,0
1,0,0 X1,0,01,1,0 X1,1,01,0,1 X1,0,11,1,1 X1,1,1
MMI-MAP estimation
Basic idea: place Dirichlet prior distribution with parameters {} on {estimate {by maximizing posterior distribution
0,0,0 X0,0,0
1,0,0 X1,0,01,1,0 X1,1,01,0,1 X1,0,11,1,1 X1,1,1
MMI-MAP estimation results
MMI-EMC2Before (with MMI-MAP): • place Dirichlet prior distribution with parameters {} on {
• user sets parameters {}• estimate {by maximizing posterior distribution
Now (with MMI-EMC2):• place hyperprior distribution on parameters {}
• user only controls few hyperparameters
• prior information about intensity built into hyperprior
• use MCMC to draw samples from posterior•Estimate posterior mean•Estimate posterior variance
Esch, Connors, Karovska, van Dyk 2004
0,0,0 X0,0,0
1,0,0 X1,0,01,1,0 X1,1,01,0,1 X1,0,11,1,1 X1,1,1
MMI - Complexity Regularization
Kolaczyk & Nowak, 2004
MMI - Complexity Regularization
pruning = aggregation = data fusion = robustness to noise
Complexity penalized estimator:
set of all possible partitions
Partitions selection
|P|
likelihood
penalty(prior)
MMI-Complexity regularization results
MMI-Complexity regularization theory
No other method can do significantly better asymptotically for this class of images!
This theory also supports other Haar-wavelet based methods!
Platelet estimation
Donoho, Ann. Stat. ‘99Willett & Nowak, IEEE-TMI ‘03
Willett & Nowak, submitted to IEEE-Info.Th. ‘05
Platelet theory
No other method can do significantly better asymptotically for this (smoother) class of images!
Platelet results
á trous wavelet transform
Holschneider 1989Starck 2002
1. Redefine wavelet as difference between scaling functions at successive levels
2. Compute coeffs. at one level by filtering coeffs at next finer scale
3. This means synthesis (getting image back from wavelet coeffs.) is simple addition
Intensity estimation with á trous wavelets
Method 1(Classical)
Compute Anscombe transform of data
Perform á trous wavelet thresholding as if iid Gaussian
noise
(same problems as other Anscombe-based approaches for very few photon counts)
Method 2(Starck + Murtagh, 2nd
ed., unpublished)
Compute variance stabilizing transform
of each á trous coefficient
Use level-dependent, wavelet-dependent, location-dependent
thresholds
(result on next slide)
á trous results
Truth Observations; 1.74
Corrected thresholds; 0.198Wavelets + Anscombe; 0.465Wavelet thresholding; 0.325
Platelets; 0.163MMI - Complexity Reg.; 0.173MMI - MAP; 0.245
Observations Wavelet thresholding
MMI - MAPCorrected thresholdsWavelets + Anscombe
A trousPlateletsMMI - Complexity Reg.
Observations Wavelet thresholding
MMI - MAPCorrected thresholdsWavelets + Anscombe
A trousPlateletsMMI - Complexity Reg.
Method Speed Effectiveness
Wavelet thresholding
Fast Poor
Wavelets + Anscombe
Fast Poor
Corrected thresholds
Fast Medium
MMI-MAP Fast Medium
MMI-EMC2 Medium High; significance maps!
MMI-Complexity regularization
Fast High
Platelets Medium-slow High
A trous Medium High