Image focus volume regularization for shape from focus through 3D weighted least squares
In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ali, Usman [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
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| Übergeordnetes Werk: |
volume:489 ; year:2019 ; pages:155-166 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.ins.2019.03.056 |
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ELV046392165 |
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| 520 | |a In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. | ||
| 520 | |a In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. | ||
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| 700 | 1 | |a Mahmood, Muhammad Tariq |4 oth | |
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10.1016/j.ins.2019.03.056 doi GBV00000000000580.pica (DE-627)ELV046392165 (ELSEVIER)S0020-0255(19)30269-5 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ali, Usman verfasserin aut Image focus volume regularization for shape from focus through 3D weighted least squares 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. Pruks, Vitalii oth Mahmood, Muhammad Tariq oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:489 year:2019 pages:155-166 extent:12 https://doi.org/10.1016/j.ins.2019.03.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 489 2019 155-166 12 |
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10.1016/j.ins.2019.03.056 doi GBV00000000000580.pica (DE-627)ELV046392165 (ELSEVIER)S0020-0255(19)30269-5 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ali, Usman verfasserin aut Image focus volume regularization for shape from focus through 3D weighted least squares 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. Pruks, Vitalii oth Mahmood, Muhammad Tariq oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:489 year:2019 pages:155-166 extent:12 https://doi.org/10.1016/j.ins.2019.03.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 489 2019 155-166 12 |
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10.1016/j.ins.2019.03.056 doi GBV00000000000580.pica (DE-627)ELV046392165 (ELSEVIER)S0020-0255(19)30269-5 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ali, Usman verfasserin aut Image focus volume regularization for shape from focus through 3D weighted least squares 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. Pruks, Vitalii oth Mahmood, Muhammad Tariq oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:489 year:2019 pages:155-166 extent:12 https://doi.org/10.1016/j.ins.2019.03.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 489 2019 155-166 12 |
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10.1016/j.ins.2019.03.056 doi GBV00000000000580.pica (DE-627)ELV046392165 (ELSEVIER)S0020-0255(19)30269-5 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ali, Usman verfasserin aut Image focus volume regularization for shape from focus through 3D weighted least squares 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. Pruks, Vitalii oth Mahmood, Muhammad Tariq oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:489 year:2019 pages:155-166 extent:12 https://doi.org/10.1016/j.ins.2019.03.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 489 2019 155-166 12 |
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10.1016/j.ins.2019.03.056 doi GBV00000000000580.pica (DE-627)ELV046392165 (ELSEVIER)S0020-0255(19)30269-5 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ali, Usman verfasserin aut Image focus volume regularization for shape from focus through 3D weighted least squares 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. Pruks, Vitalii oth Mahmood, Muhammad Tariq oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:489 year:2019 pages:155-166 extent:12 https://doi.org/10.1016/j.ins.2019.03.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 489 2019 155-166 12 |
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Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:489 year:2019 pages:155-166 extent:12 |
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Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:489 year:2019 pages:155-166 extent:12 |
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Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
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Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
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Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
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Image focus volume regularization for shape from focus through 3D weighted least squares |
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In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. |
| abstractGer |
In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. |
| abstract_unstemmed |
In shape from focus (SFF) methods, the accuracy of the depth map highly depends on the quality of image focus volume. Generally, a linear filtering or averaging using a 2D mask is applied on each slice of the focus volume to filter out the noisy focus measures. This approach may not provide optimal results due to the inherent problems associated with linear filtering. In this paper, the image focus volume is regularized by applying 3D weighted least squares (3D-WLS) approach that enhances the volume to better reconstruct the 3D shape. The weights for the regularization have been computed from the image sequence, and here image sequence acts like a structural prior and guidance volume. Such kind of guided filtering of focus volume has not been carried out earlier. 3D-WLS optimization problem has been solved in an efficient separable manner, such that the solution has been approximated by solving a sequence of 1D linear sub-problems. Sequentially for each dimension, a tridiagonal matrix is used to solve the three-point inhomogeneous Laplacian matrix. Experiments conducted on real and synthetic image sequences demonstrate the effectiveness of the proposed method. |
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