A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement
Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Khoubani, Sahar [verfasserIn] Moradi, Mohammad Hassan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 80(2020), 6 vom: 06. Nov., Seite 8999-9025 |
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| Übergeordnetes Werk: |
volume:80 ; year:2020 ; number:6 ; day:06 ; month:11 ; pages:8999-9025 |
| Links: |
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| DOI / URN: |
10.1007/s11042-020-09834-1 |
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| Katalog-ID: |
SPR043362567 |
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| 100 | 1 | |a Khoubani, Sahar |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement |
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| 520 | |a Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. | ||
| 650 | 4 | |a Quaternion wavelet |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Frame rate up-conversion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Frame interpolation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Motion compensation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Echocardiography frame rate up-conversion |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Moradi, Mohammad Hassan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 80(2020), 6 vom: 06. Nov., Seite 8999-9025 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s11042-020-09834-1 |z lizenzpflichtig |3 Volltext |
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10.1007/s11042-020-09834-1 doi (DE-627)SPR043362567 (DE-599)SPRs11042-020-09834-1-e (SPR)s11042-020-09834-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Khoubani, Sahar verfasserin aut A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 Moradi, Mohammad Hassan verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2020), 6 vom: 06. Nov., Seite 8999-9025 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2020 number:6 day:06 month:11 pages:8999-9025 https://dx.doi.org/10.1007/s11042-020-09834-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2020 6 06 11 8999-9025 |
| spelling |
10.1007/s11042-020-09834-1 doi (DE-627)SPR043362567 (DE-599)SPRs11042-020-09834-1-e (SPR)s11042-020-09834-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Khoubani, Sahar verfasserin aut A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 Moradi, Mohammad Hassan verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2020), 6 vom: 06. Nov., Seite 8999-9025 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2020 number:6 day:06 month:11 pages:8999-9025 https://dx.doi.org/10.1007/s11042-020-09834-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2020 6 06 11 8999-9025 |
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10.1007/s11042-020-09834-1 doi (DE-627)SPR043362567 (DE-599)SPRs11042-020-09834-1-e (SPR)s11042-020-09834-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Khoubani, Sahar verfasserin aut A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 Moradi, Mohammad Hassan verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2020), 6 vom: 06. Nov., Seite 8999-9025 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2020 number:6 day:06 month:11 pages:8999-9025 https://dx.doi.org/10.1007/s11042-020-09834-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2020 6 06 11 8999-9025 |
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10.1007/s11042-020-09834-1 doi (DE-627)SPR043362567 (DE-599)SPRs11042-020-09834-1-e (SPR)s11042-020-09834-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Khoubani, Sahar verfasserin aut A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 Moradi, Mohammad Hassan verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2020), 6 vom: 06. Nov., Seite 8999-9025 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2020 number:6 day:06 month:11 pages:8999-9025 https://dx.doi.org/10.1007/s11042-020-09834-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2020 6 06 11 8999-9025 |
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10.1007/s11042-020-09834-1 doi (DE-627)SPR043362567 (DE-599)SPRs11042-020-09834-1-e (SPR)s11042-020-09834-1-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Khoubani, Sahar verfasserin aut A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 Moradi, Mohammad Hassan verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 80(2020), 6 vom: 06. Nov., Seite 8999-9025 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:80 year:2020 number:6 day:06 month:11 pages:8999-9025 https://dx.doi.org/10.1007/s11042-020-09834-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 80 2020 6 06 11 8999-9025 |
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Khoubani, Sahar @@aut@@ Moradi, Mohammad Hassan @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR043362567</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111024749.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210302s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-020-09834-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR043362567</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs11042-020-09834-1-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-020-09834-1-e</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">070</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.87</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Khoubani, Sahar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. 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Khoubani, Sahar |
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Khoubani, Sahar ddc 070 bkl 54.87 misc Quaternion wavelet misc Frame rate up-conversion misc Frame interpolation misc Motion compensation misc Echocardiography frame rate up-conversion A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement |
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070 004 ASE 54.87 bkl A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement Quaternion wavelet (dpeaa)DE-He213 Frame rate up-conversion (dpeaa)DE-He213 Frame interpolation (dpeaa)DE-He213 Motion compensation (dpeaa)DE-He213 Echocardiography frame rate up-conversion (dpeaa)DE-He213 |
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ddc 070 bkl 54.87 misc Quaternion wavelet misc Frame rate up-conversion misc Frame interpolation misc Motion compensation misc Echocardiography frame rate up-conversion |
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ddc 070 bkl 54.87 misc Quaternion wavelet misc Frame rate up-conversion misc Frame interpolation misc Motion compensation misc Echocardiography frame rate up-conversion |
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fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement |
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A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement |
| abstract |
Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. |
| abstractGer |
Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. |
| abstract_unstemmed |
Abstract In this paper, we propose a fast Frame Rate Up-Conversion (FRUC) method based on Quaternion Wavelet Transform (QWT) motion estimation to improve the motion estimation accuracy and reduce the computational complexity. The proposed method contains three key elements: motion estimation, motion post-processing, and motion compensated frame interpolation. This paper considers the QWT motion estimation using the phase of image sequences, which leads to more accurate motion estimation, less post-processing procedures, and low complexity as well. Moreover, we proposed a modified fuzzy vector mean filtering to cope with motion outliers and made use of bilateral motion vector modification in the post-processing part. We have assessed our proposed method performance using six widely available benchmark test sequences and three echocardiography image sequences. The evaluations confirm that while our proposed method keeps the PSNR and SSIM performance suitable, it is at least twice faster in comparison with reference methods on the benchmark dataset. The echocardiography evaluations reveal our proposed method superiority in terms of PSNR, SSIM, and computation time, which make the proposed method suitable as an echocardiographic-specific method. |
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6 |
| title_short |
A fast quaternion wavelet-based motion compensated frame rate up-conversion with fuzzy smoothing: application to echocardiography temporal enhancement |
| url |
https://dx.doi.org/10.1007/s11042-020-09834-1 |
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true |
| author2 |
Moradi, Mohammad Hassan |
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Moradi, Mohammad Hassan |
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| doi_str |
10.1007/s11042-020-09834-1 |
| up_date |
2024-07-03T18:11:30.007Z |
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| score |
7.399728 |