To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.
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Jinsung OH, "Improved Wavelet Shrinkage Using Morphological Clustering Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 8, pp. 1962-1965, August 2002, doi: .
Abstract: To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_8_1962/_p
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@ARTICLE{e85-a_8_1962,
author={Jinsung OH, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improved Wavelet Shrinkage Using Morphological Clustering Filter},
year={2002},
volume={E85-A},
number={8},
pages={1962-1965},
abstract={To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Improved Wavelet Shrinkage Using Morphological Clustering Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1962
EP - 1965
AU - Jinsung OH
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2002
AB - To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.
ER -