An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion
Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-...
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| Autor*in: |
Liu, Hongzhe [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2022), 13 vom: 27. Dez., Seite 20139-20156 |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:13 ; day:27 ; month:12 ; pages:20139-20156 |
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| DOI / URN: |
10.1007/s11042-022-14314-9 |
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| 520 | |a Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. | ||
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| 650 | 4 | |a Image fusion | |
| 650 | 4 | |a Infrared image | |
| 650 | 4 | |a Visible image | |
| 700 | 1 | |a Yan, Hua |0 (orcid)0000-0001-9231-3175 |4 aut | |
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10.1007/s11042-022-14314-9 doi (DE-627)OLC2134624426 (DE-He213)s11042-022-14314-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Liu, Hongzhe verfasserin aut An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. Autoencoder Image fusion Infrared image Visible image Yan, Hua (orcid)0000-0001-9231-3175 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 13 vom: 27. Dez., Seite 20139-20156 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:13 day:27 month:12 pages:20139-20156 https://doi.org/10.1007/s11042-022-14314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 13 27 12 20139-20156 |
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10.1007/s11042-022-14314-9 doi (DE-627)OLC2134624426 (DE-He213)s11042-022-14314-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Liu, Hongzhe verfasserin aut An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. Autoencoder Image fusion Infrared image Visible image Yan, Hua (orcid)0000-0001-9231-3175 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 13 vom: 27. Dez., Seite 20139-20156 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:13 day:27 month:12 pages:20139-20156 https://doi.org/10.1007/s11042-022-14314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 13 27 12 20139-20156 |
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10.1007/s11042-022-14314-9 doi (DE-627)OLC2134624426 (DE-He213)s11042-022-14314-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Liu, Hongzhe verfasserin aut An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. Autoencoder Image fusion Infrared image Visible image Yan, Hua (orcid)0000-0001-9231-3175 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 13 vom: 27. Dez., Seite 20139-20156 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:13 day:27 month:12 pages:20139-20156 https://doi.org/10.1007/s11042-022-14314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 13 27 12 20139-20156 |
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10.1007/s11042-022-14314-9 doi (DE-627)OLC2134624426 (DE-He213)s11042-022-14314-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Liu, Hongzhe verfasserin aut An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. Autoencoder Image fusion Infrared image Visible image Yan, Hua (orcid)0000-0001-9231-3175 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 13 vom: 27. Dez., Seite 20139-20156 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:13 day:27 month:12 pages:20139-20156 https://doi.org/10.1007/s11042-022-14314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 13 27 12 20139-20156 |
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10.1007/s11042-022-14314-9 doi (DE-627)OLC2134624426 (DE-He213)s11042-022-14314-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Liu, Hongzhe verfasserin aut An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. Autoencoder Image fusion Infrared image Visible image Yan, Hua (orcid)0000-0001-9231-3175 aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 13 vom: 27. Dez., Seite 20139-20156 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:13 day:27 month:12 pages:20139-20156 https://doi.org/10.1007/s11042-022-14314-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 13 27 12 20139-20156 |
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An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion |
| abstract |
Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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13 |
| title_short |
An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion |
| url |
https://doi.org/10.1007/s11042-022-14314-9 |
| remote_bool |
false |
| author2 |
Yan, Hua |
| author2Str |
Yan, Hua |
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| isOA_txt |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s11042-022-14314-9 |
| up_date |
2024-07-04T01:56:02.404Z |
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| score |
7.4009476 |