CUED Publications database

Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint

Zhang, Y and Kingsbury, N (2009) Image deconvolution using a Gaussian scale mixtures model to approximate the wavelet sparseness constraint. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 681-684. ISSN 1520-6149

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Abstract

This paper proposes to use an extended Gaussian Scale Mixtures (GSM) model instead of the conventional ℓ1 norm to approximate the sparseness constraint in the wavelet domain. We combine this new constraint with subband-dependent minimization to formulate an iterative algorithm on two shift-invariant wavelet transforms, the Shannon wavelet transform and dual-tree complex wavelet transform (DTCWT). This extented GSM model introduces spatially varying information into the deconvolution process and thus enables the algorithm to achieve better results with fewer iterations in our experiments. ©2009 IEEE.

Item Type: Article
Uncontrolled Keywords: Image restoration Wavelet transforms
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:50
Last Modified: 26 Nov 2014 19:06
DOI: