Modeling and recursive state estimation for two-dimensional noncausal filters with applications in image restoration

Modeling and recursive state estimation for two-dimensional noncausal filters with applications in image restoration

Noncausal blurring filters are frequently encountered in applications of 2-D signal processing. In this paper we obtain low-order, equivalent, quarter-plane (QP) causal Roesser separable in denominator state-space models for such noncausal blurring filters. We utilize the equivalent QP causal models...

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Veröffentlicht in:IEEE transactions on circuits and systems 1987-12, Vol.34 (12), p.1507-1517
Hauptverfasser: Lele, S., Mendel, J.
Format: Artikel
Sprache:eng
Schlagworte:
Applied sciences
Circuit properties
Degradation
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Filters
Frequency filters
Image restoration
Information filtering
Pulse measurements
Quadratic programming
Radar signal processing
Recursive estimation
Signal processing algorithms
State estimation
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Zusammenfassung:Noncausal blurring filters are frequently encountered in applications of 2-D signal processing. In this paper we obtain low-order, equivalent, quarter-plane (QP) causal Roesser separable in denominator state-space models for such noncausal blurring filters. We utilize the equivalent QP causal models to develop a minimum-variance deconvolution (MVD) algorithm, which can be used in image restoration and other 2-D MVD problems.
ISSN:0098-4094
1558-1276
DOI:10.1109/TCS.1987.1086089