In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.
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Elhassane IBNELHAJ, Driss ABOUTAJDINE, "A Spatiotemporal Neuronal Filter for Channel Equalization and Video Restoration" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 10, pp. 2427-2431, October 2005, doi: 10.1093/ietisy/e88-d.10.2427.
Abstract: In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.10.2427/_p
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@ARTICLE{e88-d_10_2427,
author={Elhassane IBNELHAJ, Driss ABOUTAJDINE, },
journal={IEICE TRANSACTIONS on Information},
title={A Spatiotemporal Neuronal Filter for Channel Equalization and Video Restoration},
year={2005},
volume={E88-D},
number={10},
pages={2427-2431},
abstract={In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.},
keywords={},
doi={10.1093/ietisy/e88-d.10.2427},
ISSN={},
month={October},}
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TY - JOUR
TI - A Spatiotemporal Neuronal Filter for Channel Equalization and Video Restoration
T2 - IEICE TRANSACTIONS on Information
SP - 2427
EP - 2431
AU - Elhassane IBNELHAJ
AU - Driss ABOUTAJDINE
PY - 2005
DO - 10.1093/ietisy/e88-d.10.2427
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2005
AB - In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.
ER -