A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

Authors:
A. Pizurica;W. Philips;I. Lemahieu;M. Acheroy
Affiliations:
Dept. for Telecommun. & Inf. Process. (TELIN), Ghent Univ., Gent;-;-;-
Venue:
IEEE Transactions on Image Processing
Year:
2002

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Abstract

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.