DocumentCode :
1713876
Title :
A new complexity prior for multiresolution image denoising
Author :
Liu, Juan ; Moulin, Pierre
Author_Institution :
Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA
fYear :
1998
Firstpage :
637
Lastpage :
640
Abstract :
Application of the minimum description length (MDL) principle to multiresolution image denoising has been somewhat unsuccessful to date. This disappointing performance is due to the crudeness of the underlying prior image models, which lead to overly sparse solutions. We propose a new family of complexity priors based on Rissanen´s (1984, 1992) universal prior for integers, which produces estimates with better sparsity properties. This method vastly outperforms previous MDL schemes and is competitive with Bayesian estimators using generalized Gaussian priors on wavelet coefficients
Keywords :
AWGN; image coding; image resolution; parameter estimation; AWGN; Bayesian estimators; Rissanen´s universal prior; complexity prior; generalized Gaussian priors; image coding; image models; minimum description length; multiresolution image denoising; sparse solutions; sparsity properties; wavelet coefficients; Additive white noise; Bayesian methods; Engineering profession; Image denoising; Image representation; Image resolution; Length measurement; Particle measurements; Signal resolution; Wavelet coefficients;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Time-Frequency and Time-Scale Analysis, 1998. Proceedings of the IEEE-SP International Symposium on
Conference_Location :
Pittsburgh, PA
Print_ISBN :
0-7803-5073-1
Type :
conf
DOI :
10.1109/TFSA.1998.721505
Filename :
721505
Link To Document :
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