Truncated γ norm-based low-rank and sparse decomposition
Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yang, Zhenzhen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Low-rank and sparse decomposition |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 27 vom: 23. Apr., Seite 38279-38295 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:27 ; day:23 ; month:04 ; pages:38279-38295 |
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| DOI / URN: |
10.1007/s11042-022-12509-8 |
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| Katalog-ID: |
SPR048386979 |
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| 520 | |a Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. | ||
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| 650 | 4 | |a Generalized alternating direction method of multipliers |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image denoising |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Video foreground and background separation |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yang, Yongpeng |4 aut | |
| 700 | 1 | |a Fan, Lu |4 aut | |
| 700 | 1 | |a Bao, Bing-Kun |4 aut | |
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10.1007/s11042-022-12509-8 doi (DE-627)SPR048386979 (SPR)s11042-022-12509-8-e DE-627 ger DE-627 rakwb eng Yang, Zhenzhen verfasserin (orcid)0000-0002-5763-2768 aut Truncated γ norm-based low-rank and sparse decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. Low-rank and sparse decomposition (dpeaa)DE-He213 Truncated (dpeaa)DE-He213 norm (dpeaa)DE-He213 Generalized alternating direction method of multipliers (dpeaa)DE-He213 Image denoising (dpeaa)DE-He213 Video foreground and background separation (dpeaa)DE-He213 Yang, Yongpeng aut Fan, Lu aut Bao, Bing-Kun aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 27 vom: 23. Apr., Seite 38279-38295 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:27 day:23 month:04 pages:38279-38295 https://dx.doi.org/10.1007/s11042-022-12509-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 27 23 04 38279-38295 |
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10.1007/s11042-022-12509-8 doi (DE-627)SPR048386979 (SPR)s11042-022-12509-8-e DE-627 ger DE-627 rakwb eng Yang, Zhenzhen verfasserin (orcid)0000-0002-5763-2768 aut Truncated γ norm-based low-rank and sparse decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. Low-rank and sparse decomposition (dpeaa)DE-He213 Truncated (dpeaa)DE-He213 norm (dpeaa)DE-He213 Generalized alternating direction method of multipliers (dpeaa)DE-He213 Image denoising (dpeaa)DE-He213 Video foreground and background separation (dpeaa)DE-He213 Yang, Yongpeng aut Fan, Lu aut Bao, Bing-Kun aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 27 vom: 23. Apr., Seite 38279-38295 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:27 day:23 month:04 pages:38279-38295 https://dx.doi.org/10.1007/s11042-022-12509-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 27 23 04 38279-38295 |
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10.1007/s11042-022-12509-8 doi (DE-627)SPR048386979 (SPR)s11042-022-12509-8-e DE-627 ger DE-627 rakwb eng Yang, Zhenzhen verfasserin (orcid)0000-0002-5763-2768 aut Truncated γ norm-based low-rank and sparse decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. Low-rank and sparse decomposition (dpeaa)DE-He213 Truncated (dpeaa)DE-He213 norm (dpeaa)DE-He213 Generalized alternating direction method of multipliers (dpeaa)DE-He213 Image denoising (dpeaa)DE-He213 Video foreground and background separation (dpeaa)DE-He213 Yang, Yongpeng aut Fan, Lu aut Bao, Bing-Kun aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 27 vom: 23. Apr., Seite 38279-38295 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:27 day:23 month:04 pages:38279-38295 https://dx.doi.org/10.1007/s11042-022-12509-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 27 23 04 38279-38295 |
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10.1007/s11042-022-12509-8 doi (DE-627)SPR048386979 (SPR)s11042-022-12509-8-e DE-627 ger DE-627 rakwb eng Yang, Zhenzhen verfasserin (orcid)0000-0002-5763-2768 aut Truncated γ norm-based low-rank and sparse decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. Low-rank and sparse decomposition (dpeaa)DE-He213 Truncated (dpeaa)DE-He213 norm (dpeaa)DE-He213 Generalized alternating direction method of multipliers (dpeaa)DE-He213 Image denoising (dpeaa)DE-He213 Video foreground and background separation (dpeaa)DE-He213 Yang, Yongpeng aut Fan, Lu aut Bao, Bing-Kun aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 27 vom: 23. Apr., Seite 38279-38295 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:27 day:23 month:04 pages:38279-38295 https://dx.doi.org/10.1007/s11042-022-12509-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 27 23 04 38279-38295 |
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10.1007/s11042-022-12509-8 doi (DE-627)SPR048386979 (SPR)s11042-022-12509-8-e DE-627 ger DE-627 rakwb eng Yang, Zhenzhen verfasserin (orcid)0000-0002-5763-2768 aut Truncated γ norm-based low-rank and sparse decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. Low-rank and sparse decomposition (dpeaa)DE-He213 Truncated (dpeaa)DE-He213 norm (dpeaa)DE-He213 Generalized alternating direction method of multipliers (dpeaa)DE-He213 Image denoising (dpeaa)DE-He213 Video foreground and background separation (dpeaa)DE-He213 Yang, Yongpeng aut Fan, Lu aut Bao, Bing-Kun aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 27 vom: 23. Apr., Seite 38279-38295 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:27 day:23 month:04 pages:38279-38295 https://dx.doi.org/10.1007/s11042-022-12509-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81 2022 27 23 04 38279-38295 |
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Yang, Zhenzhen @@aut@@ Yang, Yongpeng @@aut@@ Fan, Lu @@aut@@ Bao, Bing-Kun @@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">SPR048386979</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509114057.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221019s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-022-12509-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048386979</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11042-022-12509-8-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="100" ind1="1" ind2=" "><subfield code="a">Yang, Zhenzhen</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5763-2768</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Truncated γ norm-based low-rank and sparse decomposition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. 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Yang, Zhenzhen |
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Yang, Zhenzhen Yang, Yongpeng Fan, Lu Bao, Bing-Kun |
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truncated γ norm-based low-rank and sparse decomposition |
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Truncated γ norm-based low-rank and sparse decomposition |
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Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Low-rank and sparse decomposition (LRSD) has been gained considerable attention due to its success in computer vision and many other numerous fields. However, the traditional LRSD methods have the problem of the low approximation accuracy of the rank function. To deal with this problem, the truncated γ norm is used to approximate the rank function and an improved model of truncated γ norm-based low-rank and sparse decomposition (TNLRSD) is proposed in this paper. In addition, to further improve the accuracy of the proposed model, a relaxation factor is added to the classic alternating direction method of multipliers and the generalized alternating direction method of multipliers (GADMM) is presented to solve the proposed model. Finally, simulation experiments are carried out to low-rank image denoising and video foreground and background separation to verify the effectiveness and superiority of the proposed TNLRSD method. By comparing and analysing the experimental results, we can get that the proposed TNLRSD method is more effective and robust than other LRSD methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Truncated γ norm-based low-rank and sparse decomposition |
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Yang, Yongpeng Fan, Lu Bao, Bing-Kun |
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| score |
7.398877 |