NDLI: A Kullback-Leibler divergence approach for wavelet-based blind image deconvolution

Content Provider	IEEE Xplore Digital Library
Author	Seghouane, A.-K. Hanif, M.
Copyright Year	2012
Description	Author affiliation: National ICT Australia, Canberra Research Laboratory, The Australian National University, College of Engineering and Computer Science, Australia (Seghouane, A.-K.; Hanif, M.)
Abstract	A new algorithm for wavelet-based blind image restoration is presented in this paper. It is obtained by defining an intermediate variable to characterize the original image. Both the original image and the additive noise are modeled by multivariate Gaussian process. The blurring process is specified by its point spread function, which is unknown. The original image and the blur are estimated by alternating minimization of the KullbackLeibler divergence between a model family of probability distributions defined using a linear image model and a desired family of probability distributions constrained to be concentrated on the observed data. The intermediate variable is used to introduce regularization in the algorithm. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.
Starting Page	1
Ending Page	5
File Size	226466
Page Count	5
File Format	PDF
ISBN	9781467310246
ISSN	15512541
e-ISBN	9781467310260
e-ISBN	9781467310253
DOI	10.1109/MLSP.2012.6349757
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image restoration Signal processing algorithms Data models Noise measurement Random variables Signal processing Deconvolution Gaussian scale mixture model Blind image restoration Kullback-Leibler information wavelet denoising
Content Type	Text
Resource Type	Article

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