CBA-GAN: Cartoonization style transformation based on the convolutional attention module

Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Feng [verfasserIn] Zhao, Huihuang [verfasserIn] Li, Yuhua [verfasserIn] Wu, Yichun [verfasserIn] Sun, Xianfang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks

Übergeordnetes Werk:	Enthalten in: Computers & electrical engineering - Amsterdam [u.a.] : Elsevier Science, 1973, 106
Übergeordnetes Werk:	volume:106

DOI / URN:	10.1016/j.compeleceng.2022.108575

Katalog-ID:	ELV009237631

Internformat


LEADER	01000caa a22002652 4500
001	ELV009237631
003	DE-627
005	20231103093254.0
007	cr uuu---uuuuu
008	230510s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.compeleceng.2022.108575 \|2 doi
035			\|a (DE-627)ELV009237631
035			\|a (ELSEVIER)S0045-7906(22)00790-X
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 620 \|q VZ
084			\|a 53.00 \|2 bkl
084			\|a 35.06 \|2 bkl
084			\|a 54.00 \|2 bkl
100	1		\|a Zhang, Feng \|e verfasserin \|4 aut
245	1	0	\|a CBA-GAN: Cartoonization style transformation based on the convolutional attention module
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.
650		4	\|a Cartoonization
650		4	\|a Convolutional block attention
650		4	\|a Edge detection
650		4	\|a Attention
650		4	\|a Generative adversarial networks
700	1		\|a Zhao, Huihuang \|e verfasserin \|4 aut
700	1		\|a Li, Yuhua \|e verfasserin \|4 aut
700	1		\|a Wu, Yichun \|e verfasserin \|4 aut
700	1		\|a Sun, Xianfang \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Computers & electrical engineering \|d Amsterdam [u.a.] : Elsevier Science, 1973 \|g 106 \|h Online-Ressource \|w (DE-627)306715872 \|w (DE-600)1501325-X \|w (DE-576)094531293 \|x 1879-0755 \|7 nnns
773	1	8	\|g volume:106
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
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_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
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_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 53.00 \|j Elektrotechnik: Allgemeines \|q VZ
936	b	k	\|a 35.06 \|j Computeranwendungen \|x Chemie \|q VZ
936	b	k	\|a 54.00 \|j Informatik: Allgemeines \|q VZ
951			\|a AR
952			\|d 106

Indexfelder

author_variant	f z fz h z hz y l yl y w yw x s xs
matchkey_str	article:18790755:2023----::bgnatoiaintltasomtobsdnhcnou
hierarchy_sort_str	2023
bklnumber	53.00 35.06 54.00
publishDate	2023
allfields	10.1016/j.compeleceng.2022.108575 doi (DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X DE-627 ger DE-627 rda eng 620 VZ 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Feng verfasserin aut CBA-GAN: Cartoonization style transformation based on the convolutional attention module 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks Zhao, Huihuang verfasserin aut Li, Yuhua verfasserin aut Wu, Yichun verfasserin aut Sun, Xianfang verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 106 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:106 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 Elektrotechnik: Allgemeines VZ 35.06 Computeranwendungen Chemie VZ 54.00 Informatik: Allgemeines VZ AR 106
spelling	10.1016/j.compeleceng.2022.108575 doi (DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X DE-627 ger DE-627 rda eng 620 VZ 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Feng verfasserin aut CBA-GAN: Cartoonization style transformation based on the convolutional attention module 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks Zhao, Huihuang verfasserin aut Li, Yuhua verfasserin aut Wu, Yichun verfasserin aut Sun, Xianfang verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 106 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:106 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 Elektrotechnik: Allgemeines VZ 35.06 Computeranwendungen Chemie VZ 54.00 Informatik: Allgemeines VZ AR 106
allfields_unstemmed	10.1016/j.compeleceng.2022.108575 doi (DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X DE-627 ger DE-627 rda eng 620 VZ 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Feng verfasserin aut CBA-GAN: Cartoonization style transformation based on the convolutional attention module 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks Zhao, Huihuang verfasserin aut Li, Yuhua verfasserin aut Wu, Yichun verfasserin aut Sun, Xianfang verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 106 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:106 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 Elektrotechnik: Allgemeines VZ 35.06 Computeranwendungen Chemie VZ 54.00 Informatik: Allgemeines VZ AR 106
allfieldsGer	10.1016/j.compeleceng.2022.108575 doi (DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X DE-627 ger DE-627 rda eng 620 VZ 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Feng verfasserin aut CBA-GAN: Cartoonization style transformation based on the convolutional attention module 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks Zhao, Huihuang verfasserin aut Li, Yuhua verfasserin aut Wu, Yichun verfasserin aut Sun, Xianfang verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 106 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:106 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 Elektrotechnik: Allgemeines VZ 35.06 Computeranwendungen Chemie VZ 54.00 Informatik: Allgemeines VZ AR 106
allfieldsSound	10.1016/j.compeleceng.2022.108575 doi (DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X DE-627 ger DE-627 rda eng 620 VZ 53.00 bkl 35.06 bkl 54.00 bkl Zhang, Feng verfasserin aut CBA-GAN: Cartoonization style transformation based on the convolutional attention module 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video. Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks Zhao, Huihuang verfasserin aut Li, Yuhua verfasserin aut Wu, Yichun verfasserin aut Sun, Xianfang verfasserin aut Enthalten in Computers & electrical engineering Amsterdam [u.a.] : Elsevier Science, 1973 106 Online-Ressource (DE-627)306715872 (DE-600)1501325-X (DE-576)094531293 1879-0755 nnns volume:106 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.00 Elektrotechnik: Allgemeines VZ 35.06 Computeranwendungen Chemie VZ 54.00 Informatik: Allgemeines VZ AR 106
language	English
source	Enthalten in Computers & electrical engineering 106 volume:106
sourceStr	Enthalten in Computers & electrical engineering 106 volume:106
format_phy_str_mv	Article
bklname	Elektrotechnik: Allgemeines Computeranwendungen Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers & electrical engineering
authorswithroles_txt_mv	Zhang, Feng @@aut@@ Zhao, Huihuang @@aut@@ Li, Yuhua @@aut@@ Wu, Yichun @@aut@@ Sun, Xianfang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306715872
dewey-sort	3620
id	ELV009237631
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">ELV009237631</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231103093254.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230510s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compeleceng.2022.108575</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV009237631</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7906(22)00790-X</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">rda</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">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.06</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">CBA-GAN: Cartoonization style transformation based on the convolutional attention module</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cartoonization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional block attention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Edge detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generative adversarial networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Huihuang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Yuhua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Yichun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Xianfang</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">Computers & electrical engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1973</subfield><subfield code="g">106</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306715872</subfield><subfield code="w">(DE-600)1501325-X</subfield><subfield code="w">(DE-576)094531293</subfield><subfield code="x">1879-0755</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</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_32</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_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_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</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_100</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_150</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_187</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_224</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_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_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</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_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</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_4242</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_4251</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_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</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_4393</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">53.00</subfield><subfield code="j">Elektrotechnik: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.06</subfield><subfield code="j">Computeranwendungen</subfield><subfield code="x">Chemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">106</subfield></datafield></record></collection>
author	Zhang, Feng
spellingShingle	Zhang, Feng ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Cartoonization misc Convolutional block attention misc Edge detection misc Attention misc Generative adversarial networks CBA-GAN: Cartoonization style transformation based on the convolutional attention module
authorStr	Zhang, Feng
ppnlink_with_tag_str_mv	@@773@@(DE-627)306715872
format	electronic Article
dewey-ones	620 - Engineering & allied operations
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	1879-0755
topic_title	620 VZ 53.00 bkl 35.06 bkl 54.00 bkl CBA-GAN: Cartoonization style transformation based on the convolutional attention module Cartoonization Convolutional block attention Edge detection Attention Generative adversarial networks
topic	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Cartoonization misc Convolutional block attention misc Edge detection misc Attention misc Generative adversarial networks
topic_unstemmed	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Cartoonization misc Convolutional block attention misc Edge detection misc Attention misc Generative adversarial networks
topic_browse	ddc 620 bkl 53.00 bkl 35.06 bkl 54.00 misc Cartoonization misc Convolutional block attention misc Edge detection misc Attention misc Generative adversarial networks
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Computers & electrical engineering
hierarchy_parent_id	306715872
dewey-tens	620 - Engineering
hierarchy_top_title	Computers & electrical engineering
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)306715872 (DE-600)1501325-X (DE-576)094531293
title	CBA-GAN: Cartoonization style transformation based on the convolutional attention module
ctrlnum	(DE-627)ELV009237631 (ELSEVIER)S0045-7906(22)00790-X
title_full	CBA-GAN: Cartoonization style transformation based on the convolutional attention module
author_sort	Zhang, Feng
journal	Computers & electrical engineering
journalStr	Computers & electrical engineering
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Zhang, Feng Zhao, Huihuang Li, Yuhua Wu, Yichun Sun, Xianfang
container_volume	106
class	620 VZ 53.00 bkl 35.06 bkl 54.00 bkl
format_se	Elektronische Aufsätze
author-letter	Zhang, Feng
doi_str_mv	10.1016/j.compeleceng.2022.108575
dewey-full	620
author2-role	verfasserin
title_sort	cba-gan: cartoonization style transformation based on the convolutional attention module
title_auth	CBA-GAN: Cartoonization style transformation based on the convolutional attention module
abstract	Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.
abstractGer	Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.
abstract_unstemmed	Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700
title_short	CBA-GAN: Cartoonization style transformation based on the convolutional attention module
remote_bool	true
author2	Zhao, Huihuang Li, Yuhua Wu, Yichun Sun, Xianfang
author2Str	Zhao, Huihuang Li, Yuhua Wu, Yichun Sun, Xianfang
ppnlink	306715872
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.compeleceng.2022.108575
up_date	2024-07-06T22:28:28.475Z
_version_	1803870439711178752
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">ELV009237631</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231103093254.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230510s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compeleceng.2022.108575</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV009237631</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0045-7906(22)00790-X</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">rda</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">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">53.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.06</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">CBA-GAN: Cartoonization style transformation based on the convolutional attention module</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Cartoonization is a widely practiced art form that has been integrated into every aspect of our life. Although cartoonization has made significant progress, it is still challenging to produce high-quality graphics. In this paper, a new model named Convolutional Block Attention Generative Adversarial Networks (CBA-GAN) is proposed to transform real photos into cartoonish images. The proposed method can multiply the feature images of the input image to achieve adaptive feature optimization, and can flexibly adjust the proportion of edge, texture and smoothness in the image effect, without generating redundant edges, and can better deal with shadows in the image. The experimental data set consists of content images (real scenes or photos) and style images (cartoon images), among which the content images are mainly divided into face photos, animals, food, scenes and so on. The experimental results on different types of images show that the performance of this method is better than the existing three representative methods, and it has good robustness. At the same time, the style image data set in this paper comes from animation video, therefore this method can be easily transferred to the cartoon of video.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cartoonization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convolutional block attention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Edge detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generative adversarial networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Huihuang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Yuhua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Yichun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Xianfang</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">Computers & electrical engineering</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1973</subfield><subfield code="g">106</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306715872</subfield><subfield code="w">(DE-600)1501325-X</subfield><subfield code="w">(DE-576)094531293</subfield><subfield code="x">1879-0755</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</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_32</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_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_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</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_100</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_150</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_187</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_224</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_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_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</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_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</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_4242</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_4251</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_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</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_4393</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">53.00</subfield><subfield code="j">Elektrotechnik: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.06</subfield><subfield code="j">Computeranwendungen</subfield><subfield code="x">Chemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.00</subfield><subfield code="j">Informatik: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">106</subfield></datafield></record></collection>
score	7.4009123

Nicht das Richtige dabei?

Schreiben Sie uns!

CBA-GAN: Cartoonization style transformation based on the convolutional attention module

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?