Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation
Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing...
Ausführliche Beschreibung
Gespeichert in:
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Zhu, Pan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 80(2020), 3 vom: 30. Sept., Seite 4455-4471 |
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volume:80 ; year:2020 ; number:3 ; day:30 ; month:09 ; pages:4455-4471 |
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10.1007/s11042-020-09860-z |
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| 520 | |a Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. | ||
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10.1007/s11042-020-09860-z doi (DE-627)OLC2122786701 (DE-He213)s11042-020-09860-z-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhu, Pan verfasserin (orcid)0000-0002-0873-8259 aut Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. Image fusion Sparse representation B-BEMD Common and innovation features Liu, Lu aut Zhou, Xinglin aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 30. Sept., Seite 4455-4471 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:30 month:09 pages:4455-4471 https://doi.org/10.1007/s11042-020-09860-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 30 09 4455-4471 |
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10.1007/s11042-020-09860-z doi (DE-627)OLC2122786701 (DE-He213)s11042-020-09860-z-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhu, Pan verfasserin (orcid)0000-0002-0873-8259 aut Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. Image fusion Sparse representation B-BEMD Common and innovation features Liu, Lu aut Zhou, Xinglin aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 30. Sept., Seite 4455-4471 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:30 month:09 pages:4455-4471 https://doi.org/10.1007/s11042-020-09860-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 30 09 4455-4471 |
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10.1007/s11042-020-09860-z doi (DE-627)OLC2122786701 (DE-He213)s11042-020-09860-z-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhu, Pan verfasserin (orcid)0000-0002-0873-8259 aut Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. Image fusion Sparse representation B-BEMD Common and innovation features Liu, Lu aut Zhou, Xinglin aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 30. Sept., Seite 4455-4471 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:30 month:09 pages:4455-4471 https://doi.org/10.1007/s11042-020-09860-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 30 09 4455-4471 |
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10.1007/s11042-020-09860-z doi (DE-627)OLC2122786701 (DE-He213)s11042-020-09860-z-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhu, Pan verfasserin (orcid)0000-0002-0873-8259 aut Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. Image fusion Sparse representation B-BEMD Common and innovation features Liu, Lu aut Zhou, Xinglin aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 30. Sept., Seite 4455-4471 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:30 month:09 pages:4455-4471 https://doi.org/10.1007/s11042-020-09860-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 30 09 4455-4471 |
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Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Abstract The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the “max-absolute” rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation |
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Liu, Lu Zhou, Xinglin |
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