Attention based dual path fusion networks for multi-focus image

Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu, Nana [verfasserIn] Li, Jinjiang Hua, Zhen

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Multi-focus image fusion Convolutional neural network Attention mechanism

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 8 vom: 16. Feb., Seite 10883-10906
Übergeordnetes Werk:	volume:81 ; year:2022 ; number:8 ; day:16 ; month:02 ; pages:10883-10906

Links:	Volltext

DOI / URN:	10.1007/s11042-022-12046-4

Katalog-ID:	SPR046617752

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allfields	10.1007/s11042-022-12046-4 doi (DE-627)SPR046617752 (SPR)s11042-022-12046-4-e DE-627 ger DE-627 rakwb eng Yu, Nana verfasserin aut Attention based dual path fusion networks for multi-focus image 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Jinjiang (orcid)0000-0002-2080-8678 aut Hua, Zhen aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 8 vom: 16. Feb., Seite 10883-10906 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906 https://dx.doi.org/10.1007/s11042-022-12046-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 8 16 02 10883-10906
spelling	10.1007/s11042-022-12046-4 doi (DE-627)SPR046617752 (SPR)s11042-022-12046-4-e DE-627 ger DE-627 rakwb eng Yu, Nana verfasserin aut Attention based dual path fusion networks for multi-focus image 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Jinjiang (orcid)0000-0002-2080-8678 aut Hua, Zhen aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 8 vom: 16. Feb., Seite 10883-10906 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906 https://dx.doi.org/10.1007/s11042-022-12046-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 8 16 02 10883-10906
allfields_unstemmed	10.1007/s11042-022-12046-4 doi (DE-627)SPR046617752 (SPR)s11042-022-12046-4-e DE-627 ger DE-627 rakwb eng Yu, Nana verfasserin aut Attention based dual path fusion networks for multi-focus image 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Jinjiang (orcid)0000-0002-2080-8678 aut Hua, Zhen aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 8 vom: 16. Feb., Seite 10883-10906 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906 https://dx.doi.org/10.1007/s11042-022-12046-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 8 16 02 10883-10906
allfieldsGer	10.1007/s11042-022-12046-4 doi (DE-627)SPR046617752 (SPR)s11042-022-12046-4-e DE-627 ger DE-627 rakwb eng Yu, Nana verfasserin aut Attention based dual path fusion networks for multi-focus image 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Jinjiang (orcid)0000-0002-2080-8678 aut Hua, Zhen aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 8 vom: 16. Feb., Seite 10883-10906 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906 https://dx.doi.org/10.1007/s11042-022-12046-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 8 16 02 10883-10906
allfieldsSound	10.1007/s11042-022-12046-4 doi (DE-627)SPR046617752 (SPR)s11042-022-12046-4-e DE-627 ger DE-627 rakwb eng Yu, Nana verfasserin aut Attention based dual path fusion networks for multi-focus image 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Jinjiang (orcid)0000-0002-2080-8678 aut Hua, Zhen aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 8 vom: 16. Feb., Seite 10883-10906 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906 https://dx.doi.org/10.1007/s11042-022-12046-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 8 16 02 10883-10906
language	English
source	Enthalten in Multimedia tools and applications 81(2022), 8 vom: 16. Feb., Seite 10883-10906 volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906
sourceStr	Enthalten in Multimedia tools and applications 81(2022), 8 vom: 16. Feb., Seite 10883-10906 volume:81 year:2022 number:8 day:16 month:02 pages:10883-10906
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multi-focus image fusion Convolutional neural network Attention mechanism
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Yu, Nana @@aut@@ Li, Jinjiang @@aut@@ Hua, Zhen @@aut@@
publishDateDaySort_date	2022-02-16T00:00:00Z
hierarchy_top_id	27135030X
id	SPR046617752
language_de	englisch
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However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). 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author	Yu, Nana
spellingShingle	Yu, Nana misc Multi-focus image fusion misc Convolutional neural network misc Attention mechanism Attention based dual path fusion networks for multi-focus image
authorStr	Yu, Nana
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topic_title	Attention based dual path fusion networks for multi-focus image Multi-focus image fusion (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213
topic	misc Multi-focus image fusion misc Convolutional neural network misc Attention mechanism
topic_unstemmed	misc Multi-focus image fusion misc Convolutional neural network misc Attention mechanism
topic_browse	misc Multi-focus image fusion misc Convolutional neural network misc Attention mechanism
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title	Attention based dual path fusion networks for multi-focus image
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title_full	Attention based dual path fusion networks for multi-focus image
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doi_str_mv	10.1007/s11042-022-12046-4
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title_sort	attention based dual path fusion networks for multi-focus image
title_auth	Attention based dual path fusion networks for multi-focus image
abstract	Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	Attention based dual path fusion networks for multi-focus image
url	https://dx.doi.org/10.1007/s11042-022-12046-4
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author2	Li, Jinjiang Hua, Zhen
author2Str	Li, Jinjiang Hua, Zhen
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doi_str	10.1007/s11042-022-12046-4
up_date	2024-07-03T23:34:00.499Z
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