A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising.
This paper presents a new wavelet-based image denoising method, which extends a "geometrical" Bayesian framework. The new method combines three criteria for distinguishing supposedly useful coefficients from noise: coefficient magnitudes, their evolution across scales and spatial clustering of large coefficients near image edges. These three criteria are combined in a Bayesian framework. The spatial clustering properties are expressed in a prior model. The statistical properties concerning coefficient magnitudes and their evolution across scales are expressed in a joint conditional model. The three main novelties with respect to related approaches are (1) the interscale-ratios of wavelet coefficients are statistically characterized and different local criteria for distinguishing useful coefficients from noise are evaluated, (2) a joint conditional model is introduced, and (3) a novel anisotropic Markov random field prior model is proposed. The results demonstrate an improved denoising performance over related earlier techniques.
Pizurica
Aleksandra
A
Department for Telecommunications and Information Processing, Ghent University, B-9000 Gent, Belgium. aleksandra.pizurica@telin.rug.ac.be
Philips
Wilfried
W
Lemahieu
Ignace
I
Acheroy
Marc
M