Compressed sensing image reconstruction using intra prediction
Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Song, Yun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:151 ; year:2015 ; day:3 ; month:03 ; pages:1171-1179 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2014.05.088 |
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ELV013181084 |
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| 520 | |a Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. | ||
| 520 | |a Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. | ||
| 650 | 7 | |a Intra prediction |2 Elsevier | |
| 650 | 7 | |a Postprocessing |2 Elsevier | |
| 650 | 7 | |a Total variation |2 Elsevier | |
| 650 | 7 | |a Compressed sensing |2 Elsevier | |
| 650 | 7 | |a Image reconstruction |2 Elsevier | |
| 700 | 1 | |a Cao, Wei |4 oth | |
| 700 | 1 | |a Shen, Yanfei |4 oth | |
| 700 | 1 | |a Yang, Gaobo |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
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10.1016/j.neucom.2014.05.088 doi GBVA2015014000024.pica (DE-627)ELV013181084 (ELSEVIER)S0925-2312(14)01353-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Song, Yun verfasserin aut Compressed sensing image reconstruction using intra prediction 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Intra prediction Elsevier Postprocessing Elsevier Total variation Elsevier Compressed sensing Elsevier Image reconstruction Elsevier Cao, Wei oth Shen, Yanfei oth Yang, Gaobo oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:151 year:2015 day:3 month:03 pages:1171-1179 extent:9 https://doi.org/10.1016/j.neucom.2014.05.088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 151 2015 3 0303 1171-1179 9 045F 610 |
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10.1016/j.neucom.2014.05.088 doi GBVA2015014000024.pica (DE-627)ELV013181084 (ELSEVIER)S0925-2312(14)01353-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Song, Yun verfasserin aut Compressed sensing image reconstruction using intra prediction 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Intra prediction Elsevier Postprocessing Elsevier Total variation Elsevier Compressed sensing Elsevier Image reconstruction Elsevier Cao, Wei oth Shen, Yanfei oth Yang, Gaobo oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:151 year:2015 day:3 month:03 pages:1171-1179 extent:9 https://doi.org/10.1016/j.neucom.2014.05.088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 151 2015 3 0303 1171-1179 9 045F 610 |
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10.1016/j.neucom.2014.05.088 doi GBVA2015014000024.pica (DE-627)ELV013181084 (ELSEVIER)S0925-2312(14)01353-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Song, Yun verfasserin aut Compressed sensing image reconstruction using intra prediction 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Intra prediction Elsevier Postprocessing Elsevier Total variation Elsevier Compressed sensing Elsevier Image reconstruction Elsevier Cao, Wei oth Shen, Yanfei oth Yang, Gaobo oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:151 year:2015 day:3 month:03 pages:1171-1179 extent:9 https://doi.org/10.1016/j.neucom.2014.05.088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 151 2015 3 0303 1171-1179 9 045F 610 |
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10.1016/j.neucom.2014.05.088 doi GBVA2015014000024.pica (DE-627)ELV013181084 (ELSEVIER)S0925-2312(14)01353-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Song, Yun verfasserin aut Compressed sensing image reconstruction using intra prediction 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Intra prediction Elsevier Postprocessing Elsevier Total variation Elsevier Compressed sensing Elsevier Image reconstruction Elsevier Cao, Wei oth Shen, Yanfei oth Yang, Gaobo oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:151 year:2015 day:3 month:03 pages:1171-1179 extent:9 https://doi.org/10.1016/j.neucom.2014.05.088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 151 2015 3 0303 1171-1179 9 045F 610 |
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10.1016/j.neucom.2014.05.088 doi GBVA2015014000024.pica (DE-627)ELV013181084 (ELSEVIER)S0925-2312(14)01353-8 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Song, Yun verfasserin aut Compressed sensing image reconstruction using intra prediction 2015transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. Intra prediction Elsevier Postprocessing Elsevier Total variation Elsevier Compressed sensing Elsevier Image reconstruction Elsevier Cao, Wei oth Shen, Yanfei oth Yang, Gaobo oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:151 year:2015 day:3 month:03 pages:1171-1179 extent:9 https://doi.org/10.1016/j.neucom.2014.05.088 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 151 2015 3 0303 1171-1179 9 045F 610 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. |
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Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. |
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Compressed sensing (CS) provides a general signal acquisition framework that enables the reconstruction of sparse signals from a small number of linear measurements. In this article we present a CS image reconstruction algorithm using intra prediction method based on block-based CS image framework. The current reconstruction block is firstly predicted by its surrounding reconstructed pixels, and then its prediction residual will be reconstructed. Because the sparsity level of prediction residual is higher than its original image block, the performance of our proposed CS image reconstruction algorithm is significantly superior to the traditional CS reconstruction algorithm. Furthermore, total variation model is also used to suppress the blocking artifacts caused by intra prediction and measurement noise. Experimental results also show the competitive performance with respect to peak signal-to-noise ratio and subjective visual quality. |
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Cao, Wei Shen, Yanfei Yang, Gaobo |
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