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

A. Pizurica, W. Philips, I. Lemahieu, M. Acheroy
2002 IEEE Transactions on Image Processing  
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
more » ... atistical 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. Index Terms-Image denoising, interscale ratios, Markov random field, statistical modeling, wavelets.
doi:10.1109/tip.2002.1006401 pmid:18244654 fatcat:m7mc2zfcqbcrnkopihcxmfsvnq