A Bayesian wavelet-based multidimensional deconvolution with sub-band emphasis

Author

Zhang, Yingsong ; Kingsbury, Nick

Author_Institution

Signal Processing & Communication Group, Dept. of Engineering, University of Cambridge, CB2 1PZ, UK

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

3024

Lastpage

3027

Abstract

This work proposes a new algorithm for wavelet-based multidimensional image deconvolution which employs subband-dependent minimization and the dual-tree complex wavelet transform in an iterative Bayesian framework. In addition, this algorithm employs a new prior instead of the popular ℓ1 norm, and is thus able to embed a learning scheme during the iteration which helps it to achieve better deconvolution results and faster convergence.

Keywords

Bayesian methods; Continuous wavelet transforms; Deconvolution; Iterative algorithms; Multidimensional signal processing; Multidimensional systems; Nonlinear filters; Signal processing algorithms; Wavelet domain; Wavelet transforms; Algorithms; Bayes Theorem; Computer Simulation; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Information Storage and Retrieval; Models, Statistical; Models, Theoretical; Normal Distribution; Numerical Analysis, Computer-Assisted; Reproducibility of Results; Signal Processing, Computer-Assisted;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4649840

Filename

4649840