Paper Info
Title
A Kullback-Leibler divergence approach for wavelet-based blind image deconvolution.
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.
Year
DOI
Venue
2012
10.1109/MLSP.2012.6349757
MLSP
Keywords
Field
DocType
Gaussian processes,deconvolution,image restoration,probability,wavelet transforms,Kullback-Leibler divergence approach,additive noise,blurring process,linear image model,multivariate Gaussian process,point spread function,probability distribution,wavelet-based blind image deconvolution,wavelet-based blind image restoration,Blind image restoration,Gaussian scale mixture model,Kullback-Leibler information,wavelet denoising
Blind deconvolution,Pattern recognition,Non-local means,Computer science,Deconvolution,Gaussian blur,Probability distribution,Gaussian process,Artificial intelligence,Image restoration,Kullback–Leibler divergence
Conference
ISSN
Citations 
PageRank 
2161-0363
3
0.39
References 
Authors
11
2
Name
Order
Citations
PageRank
Abd-Krim Seghouane119324.99
Muhammad Hanif220725.54