Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering
In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Panda, Jagyanseni [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Les rhinites allergiques d’origine professionnelle - Dallagi, A. ELSEVIER, 2023, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:74 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.displa.2022.102196 |
|---|
| Katalog-ID: |
ELV059108657 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV059108657 | ||
| 003 | DE-627 | ||
| 005 | 20230626052231.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221103s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.displa.2022.102196 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica |
| 035 | |a (DE-627)ELV059108657 | ||
| 035 | |a (ELSEVIER)S0141-9382(22)00038-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 100 | 1 | |a Panda, Jagyanseni |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. | ||
| 520 | |a In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. | ||
| 650 | 7 | |a Interpolation |2 Elsevier | |
| 650 | 7 | |a High resolution |2 Elsevier | |
| 650 | 7 | |a Unsharp masking |2 Elsevier | |
| 650 | 7 | |a Optimized |2 Elsevier | |
| 650 | 7 | |a Sharpening |2 Elsevier | |
| 700 | 1 | |a Meher, Sukadev |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Dallagi, A. ELSEVIER |t Les rhinites allergiques d’origine professionnelle |d 2023 |g Amsterdam [u.a.] |w (DE-627)ELV009519076 |
| 773 | 1 | 8 | |g volume:74 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.displa.2022.102196 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |d 74 |j 2022 |h 0 | ||
| author_variant |
j p jp |
|---|---|
| matchkey_str |
pandajagyansenimehersukadev:2022----:fiin2iaepclnuigtrtvotmzd |
| hierarchy_sort_str |
2022transfer abstract |
| publishDate |
2022 |
| allfields |
10.1016/j.displa.2022.102196 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 DE-627 ger DE-627 rakwb eng 610 VZ Panda, Jagyanseni verfasserin aut Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier Meher, Sukadev oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102196 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
| spelling |
10.1016/j.displa.2022.102196 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 DE-627 ger DE-627 rakwb eng 610 VZ Panda, Jagyanseni verfasserin aut Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier Meher, Sukadev oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102196 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
| allfields_unstemmed |
10.1016/j.displa.2022.102196 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 DE-627 ger DE-627 rakwb eng 610 VZ Panda, Jagyanseni verfasserin aut Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier Meher, Sukadev oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102196 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
| allfieldsGer |
10.1016/j.displa.2022.102196 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 DE-627 ger DE-627 rakwb eng 610 VZ Panda, Jagyanseni verfasserin aut Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier Meher, Sukadev oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102196 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
| allfieldsSound |
10.1016/j.displa.2022.102196 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica (DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 DE-627 ger DE-627 rakwb eng 610 VZ Panda, Jagyanseni verfasserin aut Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier Meher, Sukadev oth Enthalten in Elsevier Science Dallagi, A. ELSEVIER Les rhinites allergiques d’origine professionnelle 2023 Amsterdam [u.a.] (DE-627)ELV009519076 volume:74 year:2022 pages:0 https://doi.org/10.1016/j.displa.2022.102196 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 74 2022 0 |
| language |
English |
| source |
Enthalten in Les rhinites allergiques d’origine professionnelle Amsterdam [u.a.] volume:74 year:2022 pages:0 |
| sourceStr |
Enthalten in Les rhinites allergiques d’origine professionnelle Amsterdam [u.a.] volume:74 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Interpolation High resolution Unsharp masking Optimized Sharpening |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Les rhinites allergiques d’origine professionnelle |
| authorswithroles_txt_mv |
Panda, Jagyanseni @@aut@@ Meher, Sukadev @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV009519076 |
| dewey-sort |
3610 |
| id |
ELV059108657 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059108657</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626052231.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.displa.2022.102196</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059108657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0141-9382(22)00038-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Panda, Jagyanseni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Interpolation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">High resolution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Unsharp masking</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Optimized</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Sharpening</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meher, Sukadev</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Dallagi, A. ELSEVIER</subfield><subfield code="t">Les rhinites allergiques d’origine professionnelle</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009519076</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:74</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.displa.2022.102196</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">74</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Panda, Jagyanseni |
| spellingShingle |
Panda, Jagyanseni ddc 610 Elsevier Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| authorStr |
Panda, Jagyanseni |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV009519076 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening Elsevier |
| topic |
ddc 610 Elsevier Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening |
| topic_unstemmed |
ddc 610 Elsevier Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening |
| topic_browse |
ddc 610 Elsevier Interpolation Elsevier High resolution Elsevier Unsharp masking Elsevier Optimized Elsevier Sharpening |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
s m sm |
| hierarchy_parent_title |
Les rhinites allergiques d’origine professionnelle |
| hierarchy_parent_id |
ELV009519076 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Les rhinites allergiques d’origine professionnelle |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV009519076 |
| title |
Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| ctrlnum |
(DE-627)ELV059108657 (ELSEVIER)S0141-9382(22)00038-5 |
| title_full |
Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| author_sort |
Panda, Jagyanseni |
| journal |
Les rhinites allergiques d’origine professionnelle |
| journalStr |
Les rhinites allergiques d’origine professionnelle |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Panda, Jagyanseni |
| container_volume |
74 |
| class |
610 VZ |
| format_se |
Elektronische Aufsätze |
| author-letter |
Panda, Jagyanseni |
| doi_str_mv |
10.1016/j.displa.2022.102196 |
| dewey-full |
610 |
| title_sort |
efficient 2d image upscaling using iterative optimized sharpening filtering |
| title_auth |
Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| abstract |
In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. |
| abstractGer |
In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. |
| abstract_unstemmed |
In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA |
| title_short |
Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
| url |
https://doi.org/10.1016/j.displa.2022.102196 |
| remote_bool |
true |
| author2 |
Meher, Sukadev |
| author2Str |
Meher, Sukadev |
| ppnlink |
ELV009519076 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.displa.2022.102196 |
| up_date |
2024-07-06T20:59:53.647Z |
| _version_ |
1803864866709045248 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059108657</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626052231.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.displa.2022.102196</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001920.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059108657</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0141-9382(22)00038-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Panda, Jagyanseni</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In real-time image interpolation, polynomial-based approaches are used because they are simple and practical. The unknown pixels in the image grid are obtained using polynomial-based upscaling by taking into account the weighted average of adjacent pixels. It causes degradation in high frequency (HF) parts of the image due to blurring artifacts. Numerous edge-directed and learning-based techniques are discussed in the literature, which also introduces blurring in the image’s high variance region. To address the aforementioned issue, one pre-processing method based on the concept of unsharp masking is proposed in order to generate sharpened high resolution (HR) images from low resolution (LR) images. The LR image is blurred using a weighted average filter, which is based on the concept of unsharp masking. The difference between the LR image and the blurred LR image is then extracted to represent the loss edge portion of the image. This error or degraded HF image is sharpened iteratively using a cuckoo optimized sharpening filter known as the Iteratively Optimized Sharpening (IOS) filter. The subtle is then collected by iteratively applying ISO and combining it with the LR image using one optimized gain factor, resulting in a sharpened LR image. This pre-processing is done prior to interpolation to compensate for the loss of HF details. To up-sample the sharpened LR image, the MAKIMA scheme is used. Because of MAKIMA, the image’s edges and boundaries are preserved. As a result, an HR image with sharpened edges and texture details can be obtained. In comparison to existing techniques, the proposed algorithm yields better results in terms of visual quality and objectivity. Various image databases are used to assess the efficiency of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Interpolation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">High resolution</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Unsharp masking</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Optimized</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Sharpening</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meher, Sukadev</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Dallagi, A. ELSEVIER</subfield><subfield code="t">Les rhinites allergiques d’origine professionnelle</subfield><subfield code="d">2023</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV009519076</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:74</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.displa.2022.102196</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">74</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.400154 |