IEICE TRANSACTIONS on Fundamentals
Nonlinear Inverse Filter Using ε -Filter and Its Application to Image Restoration
Summary :
A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.
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- IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.2 pp.283-290
- Publication Date
- 2000/02/25
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- Special Section PAPER (Special Section on Intelligent Signal and Image Processing)
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Hiroaki WATABE, Kaoru ARAKAWA, Yasuhiko ARAKAWA, "Nonlinear Inverse Filter Using ε -Filter and Its Application to Image Restoration" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 2, pp. 283-290, February 2000, doi: .
Abstract: A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_2_283/_p
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@ARTICLE{e83-a_2_283,
author={Hiroaki WATABE, Kaoru ARAKAWA, Yasuhiko ARAKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Nonlinear Inverse Filter Using ε -Filter and Its Application to Image Restoration},
year={2000},
volume={E83-A},
number={2},
pages={283-290},
abstract={A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.},
keywords={},
doi={},
ISSN={},
month={February},}
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TY - JOUR
TI - Nonlinear Inverse Filter Using ε -Filter and Its Application to Image Restoration
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 283
EP - 290
AU - Hiroaki WATABE
AU - Kaoru ARAKAWA
AU - Yasuhiko ARAKAWA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2000
AB - A nonlinear inverse filter is proposed for restoring signals degraded by a linear system and additive Gaussian noise. The proposed filter consists of combination of a linear high pass filter and an ε-filter, which is modified from the cascaded linear filter. The nonlinear property of the ε-filter is utilized to suppress pre-enhanced additive random noise and to restore sharp edges. It is demonstrated that the filter can be reduced to a multi-layered neural network model, and the optimal design is described by using the back propagation algorithm. The nonlinear function is approximated by a piecewise linear function, which results in simple and robust training algorithm. An application to image restoration is also presented, illustrating the effectiveness over the linear filter, especially when the amplitude of additive noise is small.
ER -
English
