Scanned images resolution improvement using neural networks
Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Panagiotopoulou, Antigoni [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer-Verlag, 1993, 17(2007), 1 vom: 21. März, Seite 39-47 |
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| Übergeordnetes Werk: |
volume:17 ; year:2007 ; number:1 ; day:21 ; month:03 ; pages:39-47 |
| Links: |
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| DOI / URN: |
10.1007/s00521-007-0106-x |
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OLC2025580657 |
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| 520 | |a Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. | ||
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10.1007/s00521-007-0106-x doi (DE-627)OLC2025580657 (DE-He213)s00521-007-0106-x-p DE-627 ger DE-627 rakwb eng 004 VZ Panagiotopoulou, Antigoni verfasserin aut Scanned images resolution improvement using neural networks 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. Resolution improvement Neural network Scanner Anastassopoulos, Vassilis aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 1 vom: 21. März, Seite 39-47 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:1 day:21 month:03 pages:39-47 https://doi.org/10.1007/s00521-007-0106-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 1 21 03 39-47 |
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10.1007/s00521-007-0106-x doi (DE-627)OLC2025580657 (DE-He213)s00521-007-0106-x-p DE-627 ger DE-627 rakwb eng 004 VZ Panagiotopoulou, Antigoni verfasserin aut Scanned images resolution improvement using neural networks 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. Resolution improvement Neural network Scanner Anastassopoulos, Vassilis aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 1 vom: 21. März, Seite 39-47 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:1 day:21 month:03 pages:39-47 https://doi.org/10.1007/s00521-007-0106-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 1 21 03 39-47 |
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10.1007/s00521-007-0106-x doi (DE-627)OLC2025580657 (DE-He213)s00521-007-0106-x-p DE-627 ger DE-627 rakwb eng 004 VZ Panagiotopoulou, Antigoni verfasserin aut Scanned images resolution improvement using neural networks 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. Resolution improvement Neural network Scanner Anastassopoulos, Vassilis aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 1 vom: 21. März, Seite 39-47 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:1 day:21 month:03 pages:39-47 https://doi.org/10.1007/s00521-007-0106-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 1 21 03 39-47 |
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10.1007/s00521-007-0106-x doi (DE-627)OLC2025580657 (DE-He213)s00521-007-0106-x-p DE-627 ger DE-627 rakwb eng 004 VZ Panagiotopoulou, Antigoni verfasserin aut Scanned images resolution improvement using neural networks 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. Resolution improvement Neural network Scanner Anastassopoulos, Vassilis aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 1 vom: 21. März, Seite 39-47 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:1 day:21 month:03 pages:39-47 https://doi.org/10.1007/s00521-007-0106-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 1 21 03 39-47 |
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10.1007/s00521-007-0106-x doi (DE-627)OLC2025580657 (DE-He213)s00521-007-0106-x-p DE-627 ger DE-627 rakwb eng 004 VZ Panagiotopoulou, Antigoni verfasserin aut Scanned images resolution improvement using neural networks 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2007 Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. Resolution improvement Neural network Scanner Anastassopoulos, Vassilis aut Enthalten in Neural computing & applications Springer-Verlag, 1993 17(2007), 1 vom: 21. März, Seite 39-47 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:17 year:2007 number:1 day:21 month:03 pages:39-47 https://doi.org/10.1007/s00521-007-0106-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_100 GBV_ILN_152 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4046 GBV_ILN_4277 AR 17 2007 1 21 03 39-47 |
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Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. © Springer-Verlag London Limited 2007 |
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Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. © Springer-Verlag London Limited 2007 |
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Abstract A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques. © Springer-Verlag London Limited 2007 |
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