论文信息 - Implementation of two-dimensional semicausal recursive digital filters

Implementation of two-dimensional semicausal recursive digital filters

The use of semicausal or half-plane filters in image processing generally requires a large amount of extra grid points to produce the output image compared with the case employing causal or quarter-plane filters. This is due to the inherent nature of semicausal filters. In this paper, a practical aspect of implementing 2D semicausal filters for image processing is investigated in terms of state-space realization, and a method to reduce the total number of spatial grid points necessary to evaluate the output image, termed simplified implementation, is presented.

Jake K. Aggarwal | Hyokang Chang

[1] G. Marchesini,et al. State-space realization theory of two-dimensional filters , 1976 .

[2] R. Roesser. A discrete state-space model for linear image processing , 1975 .

[3] G. E. Colling,et al. New Results in 2-D Systems Theory, Part II: 2-D State-Space Models-Realization and the Notions of Controllability, Observability, and Minimality , 1977 .

[4] Donald D. Givone,et al. Minimization of Multidimensional Linear Iterative Circuits , 1973, IEEE Transactions on Computers.

[5] Sanjit K. Mitra,et al. An approach to the implementation of digital filters using microprocessors , 1978 .

[6] M. Morf,et al. New results in 2-D systems theory, part II: 2-D state-space models—Realization and the notions of controllability, observability, and minimality , 1977, Proceedings of the IEEE.

[7] M. Ekstrom,et al. Two-dimensional spectral factorization with applications in recursive digital filtering , 1976 .

[8] S. Mitra,et al. Realizations of two-dimensional recursive digital filters , 1975 .

[9] J. Aggarwal,et al. Design of two-dimensional semicasual recursive filters , 1978 .

[10] R. Eising,et al. State-space realization and inversion of 2-D systems , 1980 .