BEMD image fusion based on PCNN and compressed sensing
Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ding, Shifei [verfasserIn] Du, Peng [verfasserIn] Zhao, Xingyu [verfasserIn] Zhu, Qiangbo [verfasserIn] Xue, Yu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2018), 20 vom: 04. Okt., Seite 10045-10054 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:20 ; day:04 ; month:10 ; pages:10045-10054 |
| Links: |
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| DOI / URN: |
10.1007/s00500-018-3560-8 |
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SPR006507670 |
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| 520 | |a Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. | ||
| 650 | 4 | |a PCNN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Compressed sensing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a BEMD |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image entropy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image fusion |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Du, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Qiangbo |e verfasserin |4 aut | |
| 700 | 1 | |a Xue, Yu |e verfasserin |4 aut | |
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10.1007/s00500-018-3560-8 doi (DE-627)SPR006507670 (SPR)s00500-018-3560-8-e DE-627 ger DE-627 rakwb eng Ding, Shifei verfasserin aut BEMD image fusion based on PCNN and compressed sensing 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. PCNN (dpeaa)DE-He213 Compressed sensing (dpeaa)DE-He213 BEMD (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Du, Peng verfasserin aut Zhao, Xingyu verfasserin aut Zhu, Qiangbo verfasserin aut Xue, Yu verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 20 vom: 04. Okt., Seite 10045-10054 (DE-627)SPR006469531 nnns volume:23 year:2018 number:20 day:04 month:10 pages:10045-10054 https://dx.doi.org/10.1007/s00500-018-3560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 20 04 10 10045-10054 |
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10.1007/s00500-018-3560-8 doi (DE-627)SPR006507670 (SPR)s00500-018-3560-8-e DE-627 ger DE-627 rakwb eng Ding, Shifei verfasserin aut BEMD image fusion based on PCNN and compressed sensing 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. PCNN (dpeaa)DE-He213 Compressed sensing (dpeaa)DE-He213 BEMD (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Du, Peng verfasserin aut Zhao, Xingyu verfasserin aut Zhu, Qiangbo verfasserin aut Xue, Yu verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 20 vom: 04. Okt., Seite 10045-10054 (DE-627)SPR006469531 nnns volume:23 year:2018 number:20 day:04 month:10 pages:10045-10054 https://dx.doi.org/10.1007/s00500-018-3560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 20 04 10 10045-10054 |
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10.1007/s00500-018-3560-8 doi (DE-627)SPR006507670 (SPR)s00500-018-3560-8-e DE-627 ger DE-627 rakwb eng Ding, Shifei verfasserin aut BEMD image fusion based on PCNN and compressed sensing 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. PCNN (dpeaa)DE-He213 Compressed sensing (dpeaa)DE-He213 BEMD (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Du, Peng verfasserin aut Zhao, Xingyu verfasserin aut Zhu, Qiangbo verfasserin aut Xue, Yu verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 20 vom: 04. Okt., Seite 10045-10054 (DE-627)SPR006469531 nnns volume:23 year:2018 number:20 day:04 month:10 pages:10045-10054 https://dx.doi.org/10.1007/s00500-018-3560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 20 04 10 10045-10054 |
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10.1007/s00500-018-3560-8 doi (DE-627)SPR006507670 (SPR)s00500-018-3560-8-e DE-627 ger DE-627 rakwb eng Ding, Shifei verfasserin aut BEMD image fusion based on PCNN and compressed sensing 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. PCNN (dpeaa)DE-He213 Compressed sensing (dpeaa)DE-He213 BEMD (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Du, Peng verfasserin aut Zhao, Xingyu verfasserin aut Zhu, Qiangbo verfasserin aut Xue, Yu verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 20 vom: 04. Okt., Seite 10045-10054 (DE-627)SPR006469531 nnns volume:23 year:2018 number:20 day:04 month:10 pages:10045-10054 https://dx.doi.org/10.1007/s00500-018-3560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 20 04 10 10045-10054 |
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10.1007/s00500-018-3560-8 doi (DE-627)SPR006507670 (SPR)s00500-018-3560-8-e DE-627 ger DE-627 rakwb eng Ding, Shifei verfasserin aut BEMD image fusion based on PCNN and compressed sensing 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. PCNN (dpeaa)DE-He213 Compressed sensing (dpeaa)DE-He213 BEMD (dpeaa)DE-He213 Image entropy (dpeaa)DE-He213 Image fusion (dpeaa)DE-He213 Du, Peng verfasserin aut Zhao, Xingyu verfasserin aut Zhu, Qiangbo verfasserin aut Xue, Yu verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 20 vom: 04. Okt., Seite 10045-10054 (DE-627)SPR006469531 nnns volume:23 year:2018 number:20 day:04 month:10 pages:10045-10054 https://dx.doi.org/10.1007/s00500-018-3560-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 20 04 10 10045-10054 |
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Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. |
| abstractGer |
Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. |
| abstract_unstemmed |
Abstract Bidimensional empirical mode decomposition (BEMD) is a new method for multi-scale image decomposition. In order to forbid useless information to cause an adverse impact on results and make the process have a better self-adaptability, this paper presents a new multi-scale image fusion method, which combines pulse coupled neural network (PCNN) and compressed sensing, and uses them in the BEMD. At first, BEMD processes the original images decomposed into multiple bidimensional intrinsic mode function (BIMFs) and a residual image. Then after doing compression measurement on each layer of BIMFS, we can get compression measurement coefficients. The coefficients at the same layer do the PCNN image fusion, and we can get measurement sampling BIMFs. And then after measurement sampling BIMFs reconstructed, we can get the final BIMFs. The residual images do the fusion based on entropy weight to get the final residual image. At last, after BEMD inverse transform, the final BIMFs and the final residual image get the result image. Experimental studies have shown that compared with other multi-scale decompositions-based image fusion algorithms, the algorithm in this paper has a better performance in terms of objective criteria and visual appearance. |
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BEMD image fusion based on PCNN and compressed sensing |
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https://dx.doi.org/10.1007/s00500-018-3560-8 |
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Du, Peng Zhao, Xingyu Zhu, Qiangbo Xue, Yu |
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Du, Peng Zhao, Xingyu Zhu, Qiangbo Xue, Yu |
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2024-07-03T23:19:29.478Z |
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7.4013863 |