Wavelet-based image denoising using hidden Markov models
Guoliang Fan
Xiang-Gen Xia
Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE;
This paper appears in: Image Processing, 2000. Proceedings. 2000 International Conference on
Publication Date: 2000
Volume: 3,
On page(s): 258-261 vol.3
Meeting Date: 09/10/2000 - 09/13/2000
Location: Vancouver, BC, Canada
ISBN: 0-7803-6297-7
References Cited: 14
INSPEC Accession Number: 7005344
Digital Object Identifier: 10.1109/ICIP.2000.899344
Current Version Published: 2002-08-06
Abstract
Wavelet-domain hidden Markov models (HMMs) have been proposed and
applied to image processing, e.g., image denoising. We develop a new
HMM, called local contextual HMM (LCHMM), by introducing the Gaussian
mixture field where wavelet coefficients are assumed to locally follow
the Gaussian mixture distributions determined by their neighborhoods.
The LCHMM can exploit both the local statistics and the intrascale
dependencies of wavelet coefficients at low computational complexity. We
show that the proposed LCHMM combined with the
“cycle-spinning” technique may achieve the best performance
in image denoising
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