The popularly used circulant matrix model of deconvolution is mostly heavily ill-posed or singular and it is not suitable to many blind deconvolution problems. The aperiodic matrix model can improve the condition numb...The popularly used circulant matrix model of deconvolution is mostly heavily ill-posed or singular and it is not suitable to many blind deconvolution problems. The aperiodic matrix model can improve the condition number of deconvolution problems and its accommodation is much wider than the circulant one's. This paper discusses a comparison of the two models including their ill-posedness, the rationality of the approximation by the models, and their computational efficiency. The comparison shows that the aperiodic model is promising in the development of new restoration algorithms.展开更多
A deconvolution algorithm is proposed to account for the distortions of impulse shape introduced by propagation process. By finding the best correlation of the received waveform with the multiple templates, the number...A deconvolution algorithm is proposed to account for the distortions of impulse shape introduced by propagation process. By finding the best correlation of the received waveform with the multiple templates, the number of multipath components is reduced as the result of eliminating the ＂phantom paths＂, and the captured energy increases. Moreover, it needs only a single reference measurement in real measurement environment （do not need the anechoic chamber）, which by far simplifies the templates acquiring procedure.展开更多
An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images.Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic para...An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images.Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis,approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler(KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.展开更多
For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting...For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.展开更多
文摘The popularly used circulant matrix model of deconvolution is mostly heavily ill-posed or singular and it is not suitable to many blind deconvolution problems. The aperiodic matrix model can improve the condition number of deconvolution problems and its accommodation is much wider than the circulant one's. This paper discusses a comparison of the two models including their ill-posedness, the rationality of the approximation by the models, and their computational efficiency. The comparison shows that the aperiodic model is promising in the development of new restoration algorithms.
基金the Key Program of the National Natural Science Foundation of China (60432040)the China Postdoctors Science Foundation (20060390792).
文摘A deconvolution algorithm is proposed to account for the distortions of impulse shape introduced by propagation process. By finding the best correlation of the received waveform with the multiple templates, the number of multipath components is reduced as the result of eliminating the ＂phantom paths＂, and the captured energy increases. Moreover, it needs only a single reference measurement in real measurement environment （do not need the anechoic chamber）, which by far simplifies the templates acquiring procedure.
文摘An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images.Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis,approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler(KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.
基金supported by the National Natural Science Foundation of China(60874063)Science and Technology Research Foundation of Heilongjiang Education Department(11551355)Key Laboratory of Electronics Engineering,College of Heilongjiang Province(DZZD20105)
文摘For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.
