Computer Science – Performance
Scientific paper
Apr 1981
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1981ieeep..69..432s&link_type=abstract
IEEE, Proceedings, vol. 69, Apr. 1981, p. 432-450. Research supported by the John and Mary Franklin Foundation;
Computer Science
Performance
26
Algorithms, Iterative Solution, Noise Reduction, Signal Distortion, Signal Processing, Broadband, Convergence, Convolution Integrals, Extrapolation, Performance Prediction, Signal To Noise Ratios, Spectrum Analysis
Scientific paper
This paper describes a rather broad class of iterative signal restoration techniques which can be applied to remove the effects of many different types of distortions. These techniques also allow for the incorporation of prior knowledge of the signal in terms of the specification of a constraint operator. Conditions for convergence of the iteration under various combinations of distortions and constraints are explored. Particular attention is given to the use of iterative restoration techniques for constrained deconvolution, when the distortion bandlimits the signal and spectral extrapolation must be performed. It is shown that by predistorting the signal (and later removing this predistortion) it is possible to achieve spectral extrapolation, to broaden the class of signals for which these algorithms achieve convergence, and to improve their performance in the presence of broad-band noise.
Mersereau R. M.
Richards Anita M.
Schafer Ronald W.
No associations
LandOfFree
Constrained iterative restoration algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Constrained iterative restoration algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Constrained iterative restoration algorithms will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-782727