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

Abstract--This paper presents a new wavelet-based image denoising method, which extends a recently emerged "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 res...

• Type: Journal
• Year: 2002
• Where: TIP
• Authors: Aleksandra Pizurica, Wilfried Philips, Ignace Lemahieu, Marc Acheroy
• Keywords: Coefficient Magnitudes, Joint Conditional Model, TIP 2002, Useful Coefficients