A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise
Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of seve...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sen, Amit Prakash [verfasserIn] |
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| Format: |
E-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 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface sensing technologies and applications - Dordrecht : Springer Science Business Media B.V., 2000, 23(2022), 1 vom: 11. März |
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| Übergeordnetes Werk: |
volume:23 ; year:2022 ; number:1 ; day:11 ; month:03 |
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| DOI / URN: |
10.1007/s11220-022-00382-6 |
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| 520 | |a Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. | ||
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10.1007/s11220-022-00382-6 doi (DE-627)SPR046454241 (SPR)s11220-022-00382-6-e DE-627 ger DE-627 rakwb eng Sen, Amit Prakash verfasserin aut A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise 2022 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 2022 Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. Image de-noising (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Median filter (dpeaa)DE-He213 Trimmed filter (dpeaa)DE-He213 Adaptive median filter (dpeaa)DE-He213 Rout, Nirmal Kumar (orcid)0000-0003-1983-792X aut Enthalten in Subsurface sensing technologies and applications Dordrecht : Springer Science Business Media B.V., 2000 23(2022), 1 vom: 11. März (DE-627)320590305 (DE-600)2018843-2 1573-9317 nnns volume:23 year:2022 number:1 day:11 month:03 https://dx.doi.org/10.1007/s11220-022-00382-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 23 2022 1 11 03 |
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10.1007/s11220-022-00382-6 doi (DE-627)SPR046454241 (SPR)s11220-022-00382-6-e DE-627 ger DE-627 rakwb eng Sen, Amit Prakash verfasserin aut A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise 2022 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 2022 Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. Image de-noising (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Median filter (dpeaa)DE-He213 Trimmed filter (dpeaa)DE-He213 Adaptive median filter (dpeaa)DE-He213 Rout, Nirmal Kumar (orcid)0000-0003-1983-792X aut Enthalten in Subsurface sensing technologies and applications Dordrecht : Springer Science Business Media B.V., 2000 23(2022), 1 vom: 11. März (DE-627)320590305 (DE-600)2018843-2 1573-9317 nnns volume:23 year:2022 number:1 day:11 month:03 https://dx.doi.org/10.1007/s11220-022-00382-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 23 2022 1 11 03 |
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10.1007/s11220-022-00382-6 doi (DE-627)SPR046454241 (SPR)s11220-022-00382-6-e DE-627 ger DE-627 rakwb eng Sen, Amit Prakash verfasserin aut A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise 2022 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 2022 Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. Image de-noising (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Median filter (dpeaa)DE-He213 Trimmed filter (dpeaa)DE-He213 Adaptive median filter (dpeaa)DE-He213 Rout, Nirmal Kumar (orcid)0000-0003-1983-792X aut Enthalten in Subsurface sensing technologies and applications Dordrecht : Springer Science Business Media B.V., 2000 23(2022), 1 vom: 11. März (DE-627)320590305 (DE-600)2018843-2 1573-9317 nnns volume:23 year:2022 number:1 day:11 month:03 https://dx.doi.org/10.1007/s11220-022-00382-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 23 2022 1 11 03 |
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10.1007/s11220-022-00382-6 doi (DE-627)SPR046454241 (SPR)s11220-022-00382-6-e DE-627 ger DE-627 rakwb eng Sen, Amit Prakash verfasserin aut A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise 2022 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 2022 Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. Image de-noising (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Median filter (dpeaa)DE-He213 Trimmed filter (dpeaa)DE-He213 Adaptive median filter (dpeaa)DE-He213 Rout, Nirmal Kumar (orcid)0000-0003-1983-792X aut Enthalten in Subsurface sensing technologies and applications Dordrecht : Springer Science Business Media B.V., 2000 23(2022), 1 vom: 11. März (DE-627)320590305 (DE-600)2018843-2 1573-9317 nnns volume:23 year:2022 number:1 day:11 month:03 https://dx.doi.org/10.1007/s11220-022-00382-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 23 2022 1 11 03 |
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10.1007/s11220-022-00382-6 doi (DE-627)SPR046454241 (SPR)s11220-022-00382-6-e DE-627 ger DE-627 rakwb eng Sen, Amit Prakash verfasserin aut A Comparative Analysis of the Algorithms for De-noising Images Contaminated with Impulse Noise 2022 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 2022 Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. Image de-noising (dpeaa)DE-He213 Impulsive noise (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Median filter (dpeaa)DE-He213 Trimmed filter (dpeaa)DE-He213 Adaptive median filter (dpeaa)DE-He213 Rout, Nirmal Kumar (orcid)0000-0003-1983-792X aut Enthalten in Subsurface sensing technologies and applications Dordrecht : Springer Science Business Media B.V., 2000 23(2022), 1 vom: 11. März (DE-627)320590305 (DE-600)2018843-2 1573-9317 nnns volume:23 year:2022 number:1 day:11 month:03 https://dx.doi.org/10.1007/s11220-022-00382-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 23 2022 1 11 03 |
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Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Image pre-processing is one of the vital tasks used to redefine an image to enhance human visual perception and better information extraction. Several state-of-art have been proposed to de-noise an image contaminated with impulsive noise. This paper focuses on the study and analysis of several de-noising algorithms based on median filter and its advanced non-linear approaches. The study further concentrates on the implementation approaches proposed for de-noising impulsive noise with the deep learning technique. The study highlights the limitation of one approach and its possible solutions with other approaches proposed. The study also highlights several other issues such as optimum selection of window size, edge restoration, even number of noise-free pixels in the median filter, and its variations which have no solution so far. The performance metrics used for the evaluation of several state-of-art algorithms are peak signal-to-noise ratio (PSNR), mean absolute error, computation time and structural similarity index (SSIM). Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. The paper brings out the limitations and issues associated with the conventional and deep learning approaches for the removal of impulsive noise both subjectively and objectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Some of the recently developed algorithms such as classifier and regression model and deep convolutional neural network-based model show PSNR of 45.66 dB and 45.14 dB in 10% noise density and 28.77 dB and 29.18 dB in 90% noise density using Lena image respectively while SSIM of 0.9851 and 0.9847 in 10% noise density and 0.8116 and 0.8101 in 90% noise density using sample1 from BBBC041 dataset respectively. 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März</subfield><subfield code="w">(DE-627)320590305</subfield><subfield code="w">(DE-600)2018843-2</subfield><subfield code="x">1573-9317</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:1</subfield><subfield code="g">day:11</subfield><subfield code="g">month:03</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11220-022-00382-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2022</subfield><subfield code="e">1</subfield><subfield code="b">11</subfield><subfield code="c">03</subfield></datafield></record></collection>
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