摘要: Recursive restoration of two-dimensional noisy images gives dimensionality problems leading to large storage and computation time requirements on a digital computer. This paper shows second-order Markov process representation can be used for fast recursive with small requirements. Advantages this method over existing techniques are illustrated by means examples.
sci-hub.se 参考文章(8)
Nasser E. Nahi, Estimation Theory and Applications ,(1969)
Edward Angel, Anil Jain, A nearest neighbors approach to multidimensional filtering conference on decision and control. ,vol. 11, pp. 84- 88 ,(1972) , 10.1109/CDC.1972.268948
A. Habibi, Two-dimensional Bayesian estimate of images Proceedings of the IEEE. ,vol. 60, pp. 878- 883 ,(1972) , 10.1109/PROC.1972.8787
L. E. Franks, A Model for the Random Video Process Bell System Technical Journal. ,vol. 45, pp. 609- 630 ,(1966) , 10.1002/J.1538-7305.1966.TB01046.X
R. Bellman, John Casti, E. Angel, Dynamic Programming and Partial Differential Equations ,(2012)
D. M. Detchmendy, R. Sridhar, Sequential Estimation of States and Parameters in Noisy Nonlinear Dynamical Systems Journal of Basic Engineering. ,vol. 88, pp. 362- 368 ,(1966) , 10.1115/1.3645862
Anil Jain, Linear and nonlinear interpolation for 2-dimensional image enhancement conference on decision and control. ,vol. 11, pp. 59- 62 ,(1972) , 10.1109/CDC.1972.268941
E. Angel, A. Jain, A dimensionality reducing model for distributed filtering IEEE Transactions on Automatic Control. ,vol. 18, pp. 59- 62 ,(1973) , 10.1109/TAC.1973.1100196