A wavelet-based image denoising technique using spatial priors
Pizurica, A.
Philips, W.
Lemahieu, I.
Acheroy, M.
TELIN, Ghent Univ.;
This paper appears in: Image Processing, 2000. Proceedings. 2000 International Conference on
Publication Date: 2000
Volume: 3,
On page(s): 296-299 vol.3
Meeting Date: 09/10/2000 - 09/13/2000
Location: Vancouver, BC, Canada
ISBN: 0-7803-6297-7
References Cited: 9
INSPEC Accession Number: 7005354
Digital Object Identifier: 10.1109/ICIP.2000.899360
Current Version Published: 2002-08-06
Abstract
We propose a new wavelet-based method for image denoising that
applies the Bayesian framework, using prior knowledge about the spatial
clustering of the wavelet coefficients. Local spatial interactions of
the wavelet coefficients are modeled by adopting a Markov random field
model. An iterative updating technique known as iterated conditional
modes (ICM) is applied to estimate the binary masks containing the
positions of those wavelet coefficients that represent the useful signal
in each subband. For each wavelet coefficient a shrinkage factor is
determined, depending on its magnitude and on the local spatial
neighbourhood in the estimated mask. We derive analytically a closed
form expression for this shrinkage factor
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