IEICE TRANSACTIONS on Information
The Family of Parametric Projection Filters and Its Properties for Perturbation
Summary :
A lot of optimum filters have been proposed for an image restoration problem. Parametric filter, such as Parametric Wiener Filter, Parametric Projection Filter, or Parametric Partial Projection Filter, is often used because it requires to calculate a generalized inverse of one operator. These optimum filters are formed by a degradation operator, a covariance operator of noise, and one of original images. In practice, these operators are estimated based on empirical knowledge. Unfortunately, it happens that such operators differ from the true ones. In this paper, we show the unified formulae of inducing them to clarify their common properties. Moreover, we investigate their properties for perturbation of a degradation operator, a covariance operator of noise, and one of original images. Some numerical examples follow to confirm that our description is valid.
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- IEICE TRANSACTIONS on Information Vol.E80-D No.8 pp.788-794
- Publication Date
- 1997/08/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Hideyuki IMAI, Akira TANAKA, Masaaki MIYAKOSHI, "The Family of Parametric Projection Filters and Its Properties for Perturbation" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 8, pp. 788-794, August 1997, doi: .
Abstract: A lot of optimum filters have been proposed for an image restoration problem. Parametric filter, such as Parametric Wiener Filter, Parametric Projection Filter, or Parametric Partial Projection Filter, is often used because it requires to calculate a generalized inverse of one operator. These optimum filters are formed by a degradation operator, a covariance operator of noise, and one of original images. In practice, these operators are estimated based on empirical knowledge. Unfortunately, it happens that such operators differ from the true ones. In this paper, we show the unified formulae of inducing them to clarify their common properties. Moreover, we investigate their properties for perturbation of a degradation operator, a covariance operator of noise, and one of original images. Some numerical examples follow to confirm that our description is valid.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_8_788/_p
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@ARTICLE{e80-d_8_788,
author={Hideyuki IMAI, Akira TANAKA, Masaaki MIYAKOSHI, },
journal={IEICE TRANSACTIONS on Information},
title={The Family of Parametric Projection Filters and Its Properties for Perturbation},
year={1997},
volume={E80-D},
number={8},
pages={788-794},
abstract={A lot of optimum filters have been proposed for an image restoration problem. Parametric filter, such as Parametric Wiener Filter, Parametric Projection Filter, or Parametric Partial Projection Filter, is often used because it requires to calculate a generalized inverse of one operator. These optimum filters are formed by a degradation operator, a covariance operator of noise, and one of original images. In practice, these operators are estimated based on empirical knowledge. Unfortunately, it happens that such operators differ from the true ones. In this paper, we show the unified formulae of inducing them to clarify their common properties. Moreover, we investigate their properties for perturbation of a degradation operator, a covariance operator of noise, and one of original images. Some numerical examples follow to confirm that our description is valid.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - The Family of Parametric Projection Filters and Its Properties for Perturbation
T2 - IEICE TRANSACTIONS on Information
SP - 788
EP - 794
AU - Hideyuki IMAI
AU - Akira TANAKA
AU - Masaaki MIYAKOSHI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 1997
AB - A lot of optimum filters have been proposed for an image restoration problem. Parametric filter, such as Parametric Wiener Filter, Parametric Projection Filter, or Parametric Partial Projection Filter, is often used because it requires to calculate a generalized inverse of one operator. These optimum filters are formed by a degradation operator, a covariance operator of noise, and one of original images. In practice, these operators are estimated based on empirical knowledge. Unfortunately, it happens that such operators differ from the true ones. In this paper, we show the unified formulae of inducing them to clarify their common properties. Moreover, we investigate their properties for perturbation of a degradation operator, a covariance operator of noise, and one of original images. Some numerical examples follow to confirm that our description is valid.
ER -
English
