Deep architecture for super-resolution and deblurring of text images
Abstract Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Neji, Hala [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 83(2023), 2 vom: 20. Mai, Seite 3945-3961 |
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| Übergeordnetes Werk: |
volume:83 ; year:2023 ; number:2 ; day:20 ; month:05 ; pages:3945-3961 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-15340-x |
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| Katalog-ID: |
SPR05429259X |
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| 520 | |a Abstract Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. | ||
| 650 | 4 | |a Image super-resolution |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image deblurring |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image enhancement |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Deep CNN |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Halima, Mohamed Ben |4 aut | |
| 700 | 1 | |a Nogueras-Iso, Javier |4 aut | |
| 700 | 1 | |a Hamdani, Tarek M. |4 aut | |
| 700 | 1 | |a Qahtani, Abdulrahman M. |4 aut | |
| 700 | 1 | |a Almutiry, Omar |4 aut | |
| 700 | 1 | |a Dhahri, Habib |4 aut | |
| 700 | 1 | |a Alimi, Adel M. |4 aut | |
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10.1007/s11042-023-15340-x doi (DE-627)SPR05429259X (SPR)s11042-023-15340-x-e DE-627 ger DE-627 rakwb eng Neji, Hala verfasserin (orcid)0000-0003-3595-005X aut Deep architecture for super-resolution and deblurring of text images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 Halima, Mohamed Ben aut Nogueras-Iso, Javier aut Hamdani, Tarek M. aut Qahtani, Abdulrahman M. aut Almutiry, Omar aut Dhahri, Habib aut Alimi, Adel M. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 2 vom: 20. Mai, Seite 3945-3961 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:2 day:20 month:05 pages:3945-3961 https://dx.doi.org/10.1007/s11042-023-15340-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 2 20 05 3945-3961 |
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10.1007/s11042-023-15340-x doi (DE-627)SPR05429259X (SPR)s11042-023-15340-x-e DE-627 ger DE-627 rakwb eng Neji, Hala verfasserin (orcid)0000-0003-3595-005X aut Deep architecture for super-resolution and deblurring of text images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 Halima, Mohamed Ben aut Nogueras-Iso, Javier aut Hamdani, Tarek M. aut Qahtani, Abdulrahman M. aut Almutiry, Omar aut Dhahri, Habib aut Alimi, Adel M. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 2 vom: 20. Mai, Seite 3945-3961 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:2 day:20 month:05 pages:3945-3961 https://dx.doi.org/10.1007/s11042-023-15340-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 2 20 05 3945-3961 |
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10.1007/s11042-023-15340-x doi (DE-627)SPR05429259X (SPR)s11042-023-15340-x-e DE-627 ger DE-627 rakwb eng Neji, Hala verfasserin (orcid)0000-0003-3595-005X aut Deep architecture for super-resolution and deblurring of text images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 Halima, Mohamed Ben aut Nogueras-Iso, Javier aut Hamdani, Tarek M. aut Qahtani, Abdulrahman M. aut Almutiry, Omar aut Dhahri, Habib aut Alimi, Adel M. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 2 vom: 20. Mai, Seite 3945-3961 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:2 day:20 month:05 pages:3945-3961 https://dx.doi.org/10.1007/s11042-023-15340-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 2 20 05 3945-3961 |
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10.1007/s11042-023-15340-x doi (DE-627)SPR05429259X (SPR)s11042-023-15340-x-e DE-627 ger DE-627 rakwb eng Neji, Hala verfasserin (orcid)0000-0003-3595-005X aut Deep architecture for super-resolution and deblurring of text images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 Halima, Mohamed Ben aut Nogueras-Iso, Javier aut Hamdani, Tarek M. aut Qahtani, Abdulrahman M. aut Almutiry, Omar aut Dhahri, Habib aut Alimi, Adel M. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 2 vom: 20. Mai, Seite 3945-3961 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:2 day:20 month:05 pages:3945-3961 https://dx.doi.org/10.1007/s11042-023-15340-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 2 20 05 3945-3961 |
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10.1007/s11042-023-15340-x doi (DE-627)SPR05429259X (SPR)s11042-023-15340-x-e DE-627 ger DE-627 rakwb eng Neji, Hala verfasserin (orcid)0000-0003-3595-005X aut Deep architecture for super-resolution and deblurring of text images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 Halima, Mohamed Ben aut Nogueras-Iso, Javier aut Hamdani, Tarek M. aut Qahtani, Abdulrahman M. aut Almutiry, Omar aut Dhahri, Habib aut Alimi, Adel M. aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 2 vom: 20. Mai, Seite 3945-3961 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:2 day:20 month:05 pages:3945-3961 https://dx.doi.org/10.1007/s11042-023-15340-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 2 20 05 3945-3961 |
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Neji, Hala @@aut@@ Halima, Mohamed Ben @@aut@@ Nogueras-Iso, Javier @@aut@@ Hamdani, Tarek M. @@aut@@ Qahtani, Abdulrahman M. @@aut@@ Almutiry, Omar @@aut@@ Dhahri, Habib @@aut@@ Alimi, Adel M. @@aut@@ |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. 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Neji, Hala |
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Deep architecture for super-resolution and deblurring of text images Image super-resolution (dpeaa)DE-He213 Image deblurring (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Deep CNN (dpeaa)DE-He213 |
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deep architecture for super-resolution and deblurring of text images |
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Deep architecture for super-resolution and deblurring of text images |
| abstract |
Abstract Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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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| title_short |
Deep architecture for super-resolution and deblurring of text images |
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https://dx.doi.org/10.1007/s11042-023-15340-x |
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| author2 |
Halima, Mohamed Ben Nogueras-Iso, Javier Hamdani, Tarek M. Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. |
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Halima, Mohamed Ben Nogueras-Iso, Javier Hamdani, Tarek M. Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. |
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2024-07-04T00:54:40.179Z |
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| score |
7.4004736 |