An infrared and visible image fusion algorithm based on ResNet-152
Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResN...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Liming [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 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 7 vom: 03. Jan., Seite 9277-9287 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:7 ; day:03 ; month:01 ; pages:9277-9287 |
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| DOI / URN: |
10.1007/s11042-021-11549-w |
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OLC2078328251 |
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| 520 | |a Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. | ||
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| 650 | 4 | |a Infrared image | |
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| 700 | 1 | |a Li, Heng |4 aut | |
| 700 | 1 | |a Zhu, Rui |4 aut | |
| 700 | 1 | |a Du, Ping |4 aut | |
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10.1007/s11042-021-11549-w doi (DE-627)OLC2078328251 (DE-He213)s11042-021-11549-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhang, Liming verfasserin (orcid)0000-0001-7904-7044 aut An infrared and visible image fusion algorithm based on ResNet-152 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 2021 Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. Image processing Image fusion ResNet-152 Infrared image Visible image Li, Heng aut Zhu, Rui aut Du, Ping aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 7 vom: 03. Jan., Seite 9277-9287 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:7 day:03 month:01 pages:9277-9287 https://doi.org/10.1007/s11042-021-11549-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 7 03 01 9277-9287 |
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10.1007/s11042-021-11549-w doi (DE-627)OLC2078328251 (DE-He213)s11042-021-11549-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhang, Liming verfasserin (orcid)0000-0001-7904-7044 aut An infrared and visible image fusion algorithm based on ResNet-152 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 2021 Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. Image processing Image fusion ResNet-152 Infrared image Visible image Li, Heng aut Zhu, Rui aut Du, Ping aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 7 vom: 03. Jan., Seite 9277-9287 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:7 day:03 month:01 pages:9277-9287 https://doi.org/10.1007/s11042-021-11549-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 7 03 01 9277-9287 |
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10.1007/s11042-021-11549-w doi (DE-627)OLC2078328251 (DE-He213)s11042-021-11549-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhang, Liming verfasserin (orcid)0000-0001-7904-7044 aut An infrared and visible image fusion algorithm based on ResNet-152 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 2021 Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. Image processing Image fusion ResNet-152 Infrared image Visible image Li, Heng aut Zhu, Rui aut Du, Ping aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 7 vom: 03. Jan., Seite 9277-9287 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:7 day:03 month:01 pages:9277-9287 https://doi.org/10.1007/s11042-021-11549-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 7 03 01 9277-9287 |
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10.1007/s11042-021-11549-w doi (DE-627)OLC2078328251 (DE-He213)s11042-021-11549-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhang, Liming verfasserin (orcid)0000-0001-7904-7044 aut An infrared and visible image fusion algorithm based on ResNet-152 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 2021 Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. Image processing Image fusion ResNet-152 Infrared image Visible image Li, Heng aut Zhu, Rui aut Du, Ping aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 7 vom: 03. Jan., Seite 9277-9287 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:7 day:03 month:01 pages:9277-9287 https://doi.org/10.1007/s11042-021-11549-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 7 03 01 9277-9287 |
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10.1007/s11042-021-11549-w doi (DE-627)OLC2078328251 (DE-He213)s11042-021-11549-w-p DE-627 ger DE-627 rakwb eng 070 004 VZ Zhang, Liming verfasserin (orcid)0000-0001-7904-7044 aut An infrared and visible image fusion algorithm based on ResNet-152 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 2021 Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. Image processing Image fusion ResNet-152 Infrared image Visible image Li, Heng aut Zhu, Rui aut Du, Ping aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 7 vom: 03. Jan., Seite 9277-9287 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:7 day:03 month:01 pages:9277-9287 https://doi.org/10.1007/s11042-021-11549-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 7 03 01 9277-9287 |
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An infrared and visible image fusion algorithm based on ResNet-152 |
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Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The fusion of infrared and visible images can obtain a combined image with hidden objective and rich visible details. To improve the details of the fusion image from the infrared and visible images by reducing artifacts and noise, an infrared and visible image fusion algorithm based on ResNet-152 is proposed. First, the source images are decomposed into the low-frequency part and the high-frequency part. The low-frequency part is processed by the average weighting strategy. Second, the multi-layer features are extracted from high-frequency part by using the ResNet-152 network. Regularization L1, convolution operation, bilinear interpolation upsampling and maximum selection strategy on the feature layers to obtain the maximum weight layer. Multiplying the maximum weight layer and the high-frequency as new high-frequency. Finally, the fusion image is reconstructed by the low-frequency and the high-frequency. Experiments show that the proposed method can obtain more details from the image texture by retaining the significant features of the images. In addition, this method can effectively reduce artifacts and noise. The consistency in the objective evaluation and visual observation performs superior to the comparative algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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An infrared and visible image fusion algorithm based on ResNet-152 |
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Li, Heng Zhu, Rui Du, Ping |
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