Application of Improved Wavelet Thresholding Function in Image Denoising Processing

Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coeffic...
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Autor*in:	Hong Qi Zhang [verfasserIn] Hai Zhen Kang [verfasserIn] Li Hu Yi [verfasserIn] Yu Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Wavelet analysis Threshold function Denoising Hard threshold Soft threshold.

Übergeordnetes Werk:	In: Sensors & Transducers - IFSA Publishing, S.L., 2017, 175(2014), 7, Seite 124-131
Übergeordnetes Werk:	volume:175 ; year:2014 ; number:7 ; pages:124-131

Links:	Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ035065451

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allfieldsSound	(DE-627)DOAJ035065451 (DE-599)DOAJ333c0bb882554a12b8aa7ab52c4a3740 DE-627 ger DE-627 rakwb eng T1-995 Hong Qi Zhang verfasserin aut Application of Improved Wavelet Thresholding Function in Image Denoising Processing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantages of traditional wavelet thresholding function are studied, wavelet threshold function, the discontinuity of hard threshold and constant deviation of soft threshold are improved, image is denoised through using the improved threshold function. Wavelet analysis Threshold function Denoising Hard threshold Soft threshold. Technology (General) Hai Zhen Kang verfasserin aut Li Hu Yi verfasserin aut Yu Liu verfasserin aut In Sensors & Transducers IFSA Publishing, S.L., 2017 175(2014), 7, Seite 124-131 (DE-627)887864724 (DE-600)2894997-3 17265479 nnns volume:175 year:2014 number:7 pages:124-131 https://doaj.org/article/333c0bb882554a12b8aa7ab52c4a3740 kostenfrei http://www.sensorsportal.com/HTML/DIGEST/july_2014/Vol_175/P_2235.pdf kostenfrei https://doaj.org/toc/2306-8515 Journal toc kostenfrei https://doaj.org/toc/1726-5479 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 175 2014 7 124-131
language	English
source	In Sensors & Transducers 175(2014), 7, Seite 124-131 volume:175 year:2014 number:7 pages:124-131
sourceStr	In Sensors & Transducers 175(2014), 7, Seite 124-131 volume:175 year:2014 number:7 pages:124-131
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wavelet analysis Threshold function Denoising Hard threshold Soft threshold. Technology (General)
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container_title	Sensors & Transducers
authorswithroles_txt_mv	Hong Qi Zhang @@aut@@ Hai Zhen Kang @@aut@@ Li Hu Yi @@aut@@ Yu Liu @@aut@@
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callnumber-first	T - Technology
author	Hong Qi Zhang
spellingShingle	Hong Qi Zhang misc T1-995 misc Wavelet analysis misc Threshold function misc Denoising misc Hard threshold misc Soft threshold. misc Technology (General) Application of Improved Wavelet Thresholding Function in Image Denoising Processing
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title	Application of Improved Wavelet Thresholding Function in Image Denoising Processing
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title_full	Application of Improved Wavelet Thresholding Function in Image Denoising Processing
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title_sort	application of improved wavelet thresholding function in image denoising processing
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title_auth	Application of Improved Wavelet Thresholding Function in Image Denoising Processing
abstract	Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantages of traditional wavelet thresholding function are studied, wavelet threshold function, the discontinuity of hard threshold and constant deviation of soft threshold are improved, image is denoised through using the improved threshold function.
abstractGer	Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantages of traditional wavelet thresholding function are studied, wavelet threshold function, the discontinuity of hard threshold and constant deviation of soft threshold are improved, image is denoised through using the improved threshold function.
abstract_unstemmed	Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantages of traditional wavelet thresholding function are studied, wavelet threshold function, the discontinuity of hard threshold and constant deviation of soft threshold are improved, image is denoised through using the improved threshold function.
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title_short	Application of Improved Wavelet Thresholding Function in Image Denoising Processing
url	https://doaj.org/article/333c0bb882554a12b8aa7ab52c4a3740 http://www.sensorsportal.com/HTML/DIGEST/july_2014/Vol_175/P_2235.pdf https://doaj.org/toc/2306-8515 https://doaj.org/toc/1726-5479
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