Blind image recovery approach combing sparse and low-rank regularizations

Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demon...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng, Lei [verfasserIn] Zhu, Jun [verfasserIn] Huang, Lili [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Blind compressive sensing Nonlocal low-rank regularization Truncated Schatten- norm Alternative direction method of multipliers

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070
Übergeordnetes Werk:	volume:79 ; year:2020 ; number:25-26 ; day:27 ; month:02 ; pages:18059-18070

Links:	Volltext

DOI / URN:	10.1007/s11042-019-08575-0

Katalog-ID:	SPR040272508

Internformat


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520			\|a Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods.
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700	1		\|a Huang, Lili \|e verfasserin \|4 aut
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author_variant	l f lf j z jz l h lh
matchkey_str	article:15737721:2020----::lniaeeoeyprahobnsasadorn
hierarchy_sort_str	2020
bklnumber	54.87
publishDate	2020
allfields	10.1007/s11042-019-08575-0 doi (DE-627)SPR040272508 (SPR)s11042-019-08575-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Feng, Lei verfasserin aut Blind image recovery approach combing sparse and low-rank regularizations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods. Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213 Zhu, Jun verfasserin aut Huang, Lili verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070 https://dx.doi.org/10.1007/s11042-019-08575-0 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 79 2020 25-26 27 02 18059-18070
spelling	10.1007/s11042-019-08575-0 doi (DE-627)SPR040272508 (SPR)s11042-019-08575-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Feng, Lei verfasserin aut Blind image recovery approach combing sparse and low-rank regularizations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods. Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213 Zhu, Jun verfasserin aut Huang, Lili verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070 https://dx.doi.org/10.1007/s11042-019-08575-0 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 79 2020 25-26 27 02 18059-18070
allfields_unstemmed	10.1007/s11042-019-08575-0 doi (DE-627)SPR040272508 (SPR)s11042-019-08575-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Feng, Lei verfasserin aut Blind image recovery approach combing sparse and low-rank regularizations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods. Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213 Zhu, Jun verfasserin aut Huang, Lili verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070 https://dx.doi.org/10.1007/s11042-019-08575-0 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 79 2020 25-26 27 02 18059-18070
allfieldsGer	10.1007/s11042-019-08575-0 doi (DE-627)SPR040272508 (SPR)s11042-019-08575-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Feng, Lei verfasserin aut Blind image recovery approach combing sparse and low-rank regularizations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods. Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213 Zhu, Jun verfasserin aut Huang, Lili verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070 https://dx.doi.org/10.1007/s11042-019-08575-0 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 79 2020 25-26 27 02 18059-18070
allfieldsSound	10.1007/s11042-019-08575-0 doi (DE-627)SPR040272508 (SPR)s11042-019-08575-0-e DE-627 ger DE-627 rakwb eng 070 004 ASE 54.87 bkl Feng, Lei verfasserin aut Blind image recovery approach combing sparse and low-rank regularizations 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods. Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213 Zhu, Jun verfasserin aut Huang, Lili verfasserin aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070 https://dx.doi.org/10.1007/s11042-019-08575-0 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 79 2020 25-26 27 02 18059-18070
language	English
source	Enthalten in Multimedia tools and applications 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070
sourceStr	Enthalten in Multimedia tools and applications 79(2020), 25-26 vom: 27. Feb., Seite 18059-18070 volume:79 year:2020 number:25-26 day:27 month:02 pages:18059-18070
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Blind compressive sensing Nonlocal low-rank regularization Truncated Schatten- norm Alternative direction method of multipliers
dewey-raw	070
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Feng, Lei @@aut@@ Zhu, Jun @@aut@@ Huang, Lili @@aut@@
publishDateDaySort_date	2020-02-27T00:00:00Z
hierarchy_top_id	27135030X
dewey-sort	270
id	SPR040272508
language_de	englisch
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author	Feng, Lei
spellingShingle	Feng, Lei ddc 070 bkl 54.87 misc Blind compressive sensing misc Nonlocal low-rank regularization misc Truncated Schatten- misc norm misc Alternative direction method of multipliers Blind image recovery approach combing sparse and low-rank regularizations
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topic_title	070 004 ASE 54.87 bkl Blind image recovery approach combing sparse and low-rank regularizations Blind compressive sensing (dpeaa)DE-He213 Nonlocal low-rank regularization (dpeaa)DE-He213 Truncated Schatten- (dpeaa)DE-He213 norm (dpeaa)DE-He213 Alternative direction method of multipliers (dpeaa)DE-He213
topic	ddc 070 bkl 54.87 misc Blind compressive sensing misc Nonlocal low-rank regularization misc Truncated Schatten- misc norm misc Alternative direction method of multipliers
topic_unstemmed	ddc 070 bkl 54.87 misc Blind compressive sensing misc Nonlocal low-rank regularization misc Truncated Schatten- misc norm misc Alternative direction method of multipliers
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title	Blind image recovery approach combing sparse and low-rank regularizations
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title_full	Blind image recovery approach combing sparse and low-rank regularizations
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title_sort	blind image recovery approach combing sparse and low-rank regularizations
title_auth	Blind image recovery approach combing sparse and low-rank regularizations
abstract	Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods.
abstractGer	Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods.
abstract_unstemmed	Abstract The only useful prior knowledge in blind compressive sensing is that a signal is sparse in an unknown dictionary. Usually, general dictionaries cannot sparsify all images well. It simultaneously optimizes the dictionary and sparse coefficient in the reconstruction process and has been demonstrated to obtain same results as those compressive sensing techniques based on the known dictionary. In this paper, we propose a novel blind compressive sensing method combing sparse and low-rank regularizations to obtain competitive recovery results. We employ truncated Schatten-p norm and lq norm to approximate rank and norm. At last, we give an optimization strategy based on alternating direction method of multipliers to solve the recovery model. Experimental results prove that our approach could obtain the higher Peak Signal to Noise Ratio values than other competitive methods.
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container_issue	25-26
title_short	Blind image recovery approach combing sparse and low-rank regularizations
url	https://dx.doi.org/10.1007/s11042-019-08575-0
remote_bool	true
author2	Zhu, Jun Huang, Lili
author2Str	Zhu, Jun Huang, Lili
ppnlink	27135030X
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11042-019-08575-0
up_date	2024-07-03T14:54:39.924Z
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Blind image recovery approach combing sparse and low-rank regularizations

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