Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage

Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an init...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiao, Liang [verfasserIn] Huang, Li-Li [verfasserIn] Roysam, Badrinath [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Wavelet Shrinkage Total Variational Model Fidelity Term Staircase Effect Curvelet Coefficient

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2010(2010), 1 vom: 30. Juni
Übergeordnetes Werk:	volume:2010 ; year:2010 ; number:1 ; day:30 ; month:06

Links:	Volltext

DOI / URN:	10.1155/2010/398410

Katalog-ID:	SPR031993990

Internformat


LEADER	01000caa a22002652 4500
001	SPR031993990
003	DE-627
005	20220111195028.0
007	cr uuu---uuuuu
008	201007s2010 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1155/2010/398410 \|2 doi
035			\|a (DE-627)SPR031993990
035			\|a (SPR)398410-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 620 \|q ASE
084			\|a 53.73 \|2 bkl
100	1		\|a Xiao, Liang \|e verfasserin \|4 aut
245	1	0	\|a Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
264		1	\|c 2010
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.
650		4	\|a Wavelet Shrinkage \|7 (dpeaa)DE-He213
650		4	\|a Total Variational Model \|7 (dpeaa)DE-He213
650		4	\|a Fidelity Term \|7 (dpeaa)DE-He213
650		4	\|a Staircase Effect \|7 (dpeaa)DE-He213
650		4	\|a Curvelet Coefficient \|7 (dpeaa)DE-He213
700	1		\|a Huang, Li-Li \|e verfasserin \|4 aut
700	1		\|a Roysam, Badrinath \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t EURASIP journal on advances in signal processing \|d Heidelberg : Springer, 2007 \|g 2010(2010), 1 vom: 30. Juni \|w (DE-627)534054277 \|w (DE-600)2364203-8 \|x 1687-6180 \|7 nnns
773	1	8	\|g volume:2010 \|g year:2010 \|g number:1 \|g day:30 \|g month:06
856	4	0	\|u https://dx.doi.org/10.1155/2010/398410 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
936	b	k	\|a 53.73 \|q ASE
951			\|a AR
952			\|d 2010 \|j 2010 \|e 1 \|b 30 \|c 06

Indexfelder

author_variant	l x lx l l h llh b r br
matchkey_str	article:16876180:2010----::mgvrainleosnuigrdetieiyn
hierarchy_sort_str	2010
bklnumber	53.73
publishDate	2010
allfields	10.1155/2010/398410 doi (DE-627)SPR031993990 (SPR)398410-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Xiao, Liang verfasserin aut Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details. Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213 Huang, Li-Li verfasserin aut Roysam, Badrinath verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:06 https://dx.doi.org/10.1155/2010/398410 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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 53.73 ASE AR 2010 2010 1 30 06
spelling	10.1155/2010/398410 doi (DE-627)SPR031993990 (SPR)398410-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Xiao, Liang verfasserin aut Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details. Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213 Huang, Li-Li verfasserin aut Roysam, Badrinath verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:06 https://dx.doi.org/10.1155/2010/398410 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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 53.73 ASE AR 2010 2010 1 30 06
allfields_unstemmed	10.1155/2010/398410 doi (DE-627)SPR031993990 (SPR)398410-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Xiao, Liang verfasserin aut Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details. Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213 Huang, Li-Li verfasserin aut Roysam, Badrinath verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:06 https://dx.doi.org/10.1155/2010/398410 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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 53.73 ASE AR 2010 2010 1 30 06
allfieldsGer	10.1155/2010/398410 doi (DE-627)SPR031993990 (SPR)398410-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Xiao, Liang verfasserin aut Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details. Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213 Huang, Li-Li verfasserin aut Roysam, Badrinath verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:06 https://dx.doi.org/10.1155/2010/398410 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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 53.73 ASE AR 2010 2010 1 30 06
allfieldsSound	10.1155/2010/398410 doi (DE-627)SPR031993990 (SPR)398410-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Xiao, Liang verfasserin aut Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details. Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213 Huang, Li-Li verfasserin aut Roysam, Badrinath verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Juni (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:06 https://dx.doi.org/10.1155/2010/398410 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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 53.73 ASE AR 2010 2010 1 30 06
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 30. Juni volume:2010 year:2010 number:1 day:30 month:06
sourceStr	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 30. Juni volume:2010 year:2010 number:1 day:30 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wavelet Shrinkage Total Variational Model Fidelity Term Staircase Effect Curvelet Coefficient
dewey-raw	620
isfreeaccess_bool	true
container_title	EURASIP journal on advances in signal processing
authorswithroles_txt_mv	Xiao, Liang @@aut@@ Huang, Li-Li @@aut@@ Roysam, Badrinath @@aut@@
publishDateDaySort_date	2010-06-30T00:00:00Z
hierarchy_top_id	534054277
dewey-sort	3620
id	SPR031993990
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR031993990</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111195028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2010 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1155/2010/398410</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR031993990</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)398410-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Liang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wavelet Shrinkage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Total Variational Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fidelity Term</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Staircase Effect</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Curvelet Coefficient</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Li-Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roysam, Badrinath</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EURASIP journal on advances in signal processing</subfield><subfield code="d">Heidelberg : Springer, 2007</subfield><subfield code="g">2010(2010), 1 vom: 30. Juni</subfield><subfield code="w">(DE-627)534054277</subfield><subfield code="w">(DE-600)2364203-8</subfield><subfield code="x">1687-6180</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2010</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:1</subfield><subfield code="g">day:30</subfield><subfield code="g">month:06</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1155/2010/398410</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2010</subfield><subfield code="j">2010</subfield><subfield code="e">1</subfield><subfield code="b">30</subfield><subfield code="c">06</subfield></datafield></record></collection>
author	Xiao, Liang
spellingShingle	Xiao, Liang ddc 620 bkl 53.73 misc Wavelet Shrinkage misc Total Variational Model misc Fidelity Term misc Staircase Effect misc Curvelet Coefficient Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
authorStr	Xiao, Liang
ppnlink_with_tag_str_mv	@@773@@(DE-627)534054277
format	electronic Article
dewey-ones	620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1687-6180
topic_title	620 ASE 53.73 bkl Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage Wavelet Shrinkage (dpeaa)DE-He213 Total Variational Model (dpeaa)DE-He213 Fidelity Term (dpeaa)DE-He213 Staircase Effect (dpeaa)DE-He213 Curvelet Coefficient (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Wavelet Shrinkage misc Total Variational Model misc Fidelity Term misc Staircase Effect misc Curvelet Coefficient
topic_unstemmed	ddc 620 bkl 53.73 misc Wavelet Shrinkage misc Total Variational Model misc Fidelity Term misc Staircase Effect misc Curvelet Coefficient
topic_browse	ddc 620 bkl 53.73 misc Wavelet Shrinkage misc Total Variational Model misc Fidelity Term misc Staircase Effect misc Curvelet Coefficient
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	EURASIP journal on advances in signal processing
hierarchy_parent_id	534054277
dewey-tens	620 - Engineering
hierarchy_top_title	EURASIP journal on advances in signal processing
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)534054277 (DE-600)2364203-8
title	Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
ctrlnum	(DE-627)SPR031993990 (SPR)398410-e
title_full	Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
author_sort	Xiao, Liang
journal	EURASIP journal on advances in signal processing
journalStr	EURASIP journal on advances in signal processing
lang_code	eng
isOA_bool	true
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2010
contenttype_str_mv	txt
author_browse	Xiao, Liang Huang, Li-Li Roysam, Badrinath
container_volume	2010
class	620 ASE 53.73 bkl
format_se	Elektronische Aufsätze
author-letter	Xiao, Liang
doi_str_mv	10.1155/2010/398410
dewey-full	620
author2-role	verfasserin
title_sort	image variational denoising using gradient fidelity on curvelet shrinkage
title_auth	Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
abstract	Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.
abstractGer	Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.
abstract_unstemmed	Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2522 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
container_issue	1
title_short	Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage
url	https://dx.doi.org/10.1155/2010/398410
remote_bool	true
author2	Huang, Li-Li Roysam, Badrinath
author2Str	Huang, Li-Li Roysam, Badrinath
ppnlink	534054277
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1155/2010/398410
up_date	2024-07-04T02:05:18.162Z
_version_	1803612290456485888
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR031993990</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111195028.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2010 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1155/2010/398410</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR031993990</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)398410-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Liang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A new variational image model is presented for image restoration using a combination of the curvelet shrinkage method and the total variation (TV) functional. In order to suppress the staircasing effect and curvelet-like artifacts, we use the multiscale curvelet shrinkage to compute an initial estimated image, and then we propose a new gradient fidelity term, which is designed to force the gradients of desired image to be close to the curvelet approximation gradients. Then, we introduce the Euler-Lagrange equation and make an investigation on the mathematical properties. To improve the ability of preserving the details of edges and texture, the spatial-varying parameters are adaptively estimated in the iterative process of the gradient descent flow algorithm. Numerical experiments demonstrate that our proposed method has good performance in alleviating both the staircasing effect and curvelet-like artifacts, while preserving fine details.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wavelet Shrinkage</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Total Variational Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fidelity Term</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Staircase Effect</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Curvelet Coefficient</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Li-Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Roysam, Badrinath</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">EURASIP journal on advances in signal processing</subfield><subfield code="d">Heidelberg : Springer, 2007</subfield><subfield code="g">2010(2010), 1 vom: 30. Juni</subfield><subfield code="w">(DE-627)534054277</subfield><subfield code="w">(DE-600)2364203-8</subfield><subfield code="x">1687-6180</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2010</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:1</subfield><subfield code="g">day:30</subfield><subfield code="g">month:06</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1155/2010/398410</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">53.73</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2010</subfield><subfield code="j">2010</subfield><subfield code="e">1</subfield><subfield code="b">30</subfield><subfield code="c">06</subfield></datafield></record></collection>
score	7.4008055

Nicht das Richtige dabei?

Schreiben Sie uns!

Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?