Paper Info
Title
Undecimated haar thresholding for poisson intensity estimation
Abstract
We propose a novel algorithm for denoising Poisson-corrupted images, that performs a signal-adaptive thresholding of the undecimated Haar wavelet coefficients. A Poisson's unbiased MSE estimate is devised and adapted to arbitrary transform-domain pointwise processing. This prior-free quadratic measure of quality is then used to globally optimize a linearly parameterized subband-adaptive thresholding, which accounts for the signal-dependent noise variance. We demonstrate the qualitative and computational competitiveness of the resulting denoising algorithm through comprehensive comparisons with some state-of-the-art multiscale techniques specifically designed for Poisson intensity estimation. We also show promising denoising results obtained on low-count fluorescence microscopy images.
Year
DOI
Venue
2010
10.1109/ICIP.2010.5652184
Image Processing
Keywords
Field
DocType
Haar transforms,image denoising,mean square error methods,stochastic processes,Haar wavelet coefficients,MSE estimation,Poisson intensity estimation,image denoising,low-count fluorescence microscopy images,mean square error estimation,prior-free quadratic measure,signal-adaptive thresholding,signal-dependent noise variance,subband-adaptive thresholding,transform-domain point- wise processing,undecimated Haar thresholding,Haar wavelet,Image denoising,MSE estimation,Poisson noise,fluorescence microscopy
Noise reduction,Pattern recognition,Computer science,Haar,Artificial intelligence,Thresholding,Haar wavelet,Poisson distribution,Shot noise,Pointwise,Wavelet transform
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4244-7993-1
978-1-4244-7993-1
6
PageRank 
References 
Authors
0.53
8
3
Name
Order
Citations
PageRank
F. Luisier144722.09
T Blu22574259.70
M Unser34335499.89