The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
중요한 웨이블릿 계수를 에지 영역과 노이즈 영역으로 분류하기 위해 웨이블릿 수축에 적용되는 형태학적 클러스터링 필터가 도입되었습니다. 형태학적 클러스터링 필터를 사용하는 웨이블릿 축소에 대한 새로운 방법이 잡음 제거에 사용되며 다양한 잡음 조건에서 성능이 평가됩니다.
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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
부
@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},}
부
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 -