Title :
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
Author :
Pizurica, Aleksandra ; Philips, Wilfried ; Lemahieu, Ignace ; Acheroy, Marc
Author_Institution :
Dept. for Telecommun. & Inf. Process. (TELIN), Ghent Univ., Gent, Belgium
fDate :
5/1/2002 12:00:00 AM
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.
Keywords :
Bayes methods; Markov processes; image processing; noise; random processes; statistical analysis; wavelet transforms; Bayesian wavelet based image denoising; anisotropic Markov random field prior model; coefficient magnitudes; denoising performance; geometrical Bayesian framework; image edges; interscale statistical model; interscale-ratios; intrascale statistical model; joint conditional model; noise reduction; spatial clustering; statistical properties; wavelet coefficients; Adaptive algorithm; Anisotropic magnetoresistance; Bayesian methods; Image coding; Image denoising; Image resolution; Markov random fields; Noise reduction; Spatial resolution; Wavelet coefficients;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2002.1006401