Image compressive sensing via Truncated Schatten-p Norm regularization
Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Feng, Lei [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
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2016transfer abstract |
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14 |
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| Übergeordnetes Werk: |
Enthalten in: Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis - Zeng, C. ELSEVIER, 2014, theory, techniques & applications, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:47 ; year:2016 ; pages:28-41 ; extent:14 |
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| DOI / URN: |
10.1016/j.image.2016.05.012 |
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| 520 | |a Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. | ||
| 520 | |a Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. | ||
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| 650 | 7 | |a Truncated Schatten-p Norm |2 Elsevier | |
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| 700 | 1 | |a Sun, Quansen |4 oth | |
| 700 | 1 | |a Xia, Guiyu |4 oth | |
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10.1016/j.image.2016.05.012 doi GBVA2016014000020.pica (DE-627)ELV035378018 (ELSEVIER)S0923-5965(16)30079-0 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 610 VZ 660 620 VZ 52.56 bkl Feng, Lei verfasserin aut Image compressive sensing via Truncated Schatten-p Norm regularization 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Compressive sensing Elsevier Truncated Schatten-p Norm Elsevier Alternative direction multiplier method Elsevier Sun, Huaijiang oth Sun, Quansen oth Xia, Guiyu oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:47 year:2016 pages:28-41 extent:14 https://doi.org/10.1016/j.image.2016.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 47 2016 28-41 14 045F 004 |
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10.1016/j.image.2016.05.012 doi GBVA2016014000020.pica (DE-627)ELV035378018 (ELSEVIER)S0923-5965(16)30079-0 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 610 VZ 660 620 VZ 52.56 bkl Feng, Lei verfasserin aut Image compressive sensing via Truncated Schatten-p Norm regularization 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Compressive sensing Elsevier Truncated Schatten-p Norm Elsevier Alternative direction multiplier method Elsevier Sun, Huaijiang oth Sun, Quansen oth Xia, Guiyu oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:47 year:2016 pages:28-41 extent:14 https://doi.org/10.1016/j.image.2016.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 47 2016 28-41 14 045F 004 |
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10.1016/j.image.2016.05.012 doi GBVA2016014000020.pica (DE-627)ELV035378018 (ELSEVIER)S0923-5965(16)30079-0 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 610 VZ 660 620 VZ 52.56 bkl Feng, Lei verfasserin aut Image compressive sensing via Truncated Schatten-p Norm regularization 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Compressive sensing Elsevier Truncated Schatten-p Norm Elsevier Alternative direction multiplier method Elsevier Sun, Huaijiang oth Sun, Quansen oth Xia, Guiyu oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:47 year:2016 pages:28-41 extent:14 https://doi.org/10.1016/j.image.2016.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 47 2016 28-41 14 045F 004 |
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10.1016/j.image.2016.05.012 doi GBVA2016014000020.pica (DE-627)ELV035378018 (ELSEVIER)S0923-5965(16)30079-0 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 610 VZ 660 620 VZ 52.56 bkl Feng, Lei verfasserin aut Image compressive sensing via Truncated Schatten-p Norm regularization 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Compressive sensing Elsevier Truncated Schatten-p Norm Elsevier Alternative direction multiplier method Elsevier Sun, Huaijiang oth Sun, Quansen oth Xia, Guiyu oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:47 year:2016 pages:28-41 extent:14 https://doi.org/10.1016/j.image.2016.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 47 2016 28-41 14 045F 004 |
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10.1016/j.image.2016.05.012 doi GBVA2016014000020.pica (DE-627)ELV035378018 (ELSEVIER)S0923-5965(16)30079-0 DE-627 ger DE-627 rakwb eng 004 000 004 DE-600 000 DE-600 610 VZ 660 620 VZ 52.56 bkl Feng, Lei verfasserin aut Image compressive sensing via Truncated Schatten-p Norm regularization 2016transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. Compressive sensing Elsevier Truncated Schatten-p Norm Elsevier Alternative direction multiplier method Elsevier Sun, Huaijiang oth Sun, Quansen oth Xia, Guiyu oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:47 year:2016 pages:28-41 extent:14 https://doi.org/10.1016/j.image.2016.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 47 2016 28-41 14 045F 004 |
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Enthalten in Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis Amsterdam [u.a.] volume:47 year:2016 pages:28-41 extent:14 |
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Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |
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Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. |
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Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. |
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Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low-rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. |
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Image compressive sensing via Truncated Schatten-p Norm regularization |
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