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
Autor*in: |
Panda, Jagyanseni [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Les rhinites allergiques d’origine professionnelle - Dallagi, A. ELSEVIER, 2023, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:74 ; year:2022 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.displa.2022.102196 |
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Katalog-ID: |
ELV059108657 |
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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. | ||
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650 | 7 | |a Sharpening |2 Elsevier | |
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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 |
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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 |
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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 |
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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 |
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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 |
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Efficient 2D image Upscaling using Iterative Optimized Sharpening filtering |
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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. |
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