Abstract
A novel method based Full Bayesian Model for Neural Network (FBMNN) to study the statistical dependency of wavelet coefficients is presented. To overcome the ignorance of the relationship between wavelet coefficients, we introduce the FBMNN to model joint probability density distribution (JPDF) of Child and Parent wavelet coefficients. According to the characteristics of the suggested FBMNN-JPDF model, its parameters are estimated by reversible jump MCMC (rjMCMC) algorithm. Finally, a practical application on denoising image by using the FBMNN-JPDF model is demonstrated and the result shows that the suggested method can express wavelet coefficients dependency efficiently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Crouse, M.: Wavelet-based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans.Signal Processing. 46, 886–902 (1998)
Andrieu, C.: Sequential Bayesian Estimation and Model Selection Applied to Neural Networks. Technical Report CUED/F-INFENG/TR341,Cambridge University
Mallat, S.: Singularity Detection and Processing with Wavelets. IEEE Transaction on information theory 38, 617–643 (1992)
Green, P.J.: Reversible Jump MCMC Computation and Bayesian Model Determination. Biometrika 82, 711–732
Sendur, L.: Subband Adaptive Image Denosing via Bivariate Shrinkage. IEEE ICIP, III577–III 580 (2002)
Xing, L., Jing, Z.: A Novel Image-denoising Method Based on Mixture Gaussian Model. In: Proceedings of the 6th international progress on WAAMT. World Scientific Publishing Co., Singapore (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Long, X., Zhou, J. (2009). Statistical Dependency of Image Wavelet Coefficients: Full Bayesian Model for Neural Networks. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-01507-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
eBook Packages: Computer ScienceComputer Science (R0)