IEEE Trans Image Process - A Kullback-Leibler divergence approach to blind image restoration.

Tópicos

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Resumo

A new algorithm for maximum-likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the linear image degradation model and a desired family of probability distributions constrained to be concentrated on the observed data. 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.

Resumo Limpo

new algorithm maximumlikelihood blind imag restor present paper obtain model origin imag addit nois multivari gaussian process unknown covari matric blur process specifi point spread function also unknown estim origin imag blur deriv altern minim kullbackleibl diverg model famili probabl distribut defin use linear imag degrad model desir famili probabl distribut constrain concentr observ data algorithm present advantag provid close form express paramet updat converg iter simul exampl illustr effect propos algorithm present

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