TY - GEN
T1 - A Kullback-Leibler divergence approach for wavelet-based blind image deconvolution
AU - Seghouane, Abd Krim
AU - Hanif, Muhammad
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Blind image restoration
KW - Gaussian scale mixture model
KW - Kullback-Leibler information
KW - wavelet denoising
UR - http://www.scopus.com/inward/record.url?scp=84870706209&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2012.6349757
DO - 10.1109/MLSP.2012.6349757
M3 - Conference contribution
AN - SCOPUS:84870706209
SN - 9781467310260
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
T2 - 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Y2 - 23 September 2012 through 26 September 2012
ER -