Abstract

This paper realizes a computational integral imaging reconstruction method via scale invariant feature transform (SIFT) and patch matching to improve the visual quality of reconstructed 3D view images. To our knowledge, the 3D view images reconstructed from the elemental images suffer from artifacts, which leads to degradations in the visual quality. To prevent image degradation, in this paper, we use the correct regions obtained from the view images taken directly from the original object or use patch matching to replace the distorted regions. However, the initial matching regions could not meet our requirements owing to the limitations of the equipment and the inevitable shortcomings of the experimental operation. To solve these problems, we adopt SIFT descriptors and perspective transform to get the satisfying correct regions. We present the simulation and experimental results of the 3D view images and the evaluation of the quality of the corresponding images to test the performance of the proposed method. The simulation and experimental results indicate that the proposed method can significantly improve the visual quality of the 3D view images and verify the feasibility and effectiveness of the proposed method.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (1)

X. Li, Y. Wang, Q. H. Wang, Y. Liu, and X. Zhou, “Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm,” Opt. Lasers Eng. 112, 162–169 (2019).
[Crossref]

2018 (3)

X. Li, M. Zhao, Y. Xing, H. L. Zhang, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Designing optical 3d images encryption and reconstruction using monospectral synthetic aperture integral imaging,” Opt. Express 26(9), 11084–11099 (2018).
[Crossref] [PubMed]

K. Inoue, M. Lee, B. Javidi, and M. Cho, “Improved 3D integral imaging reconstruction with elemental image pixel rearrangement,” J. Opt. 20(2), 025703 (2018).
[Crossref]

B. Cho, P. Kopycki, M. Martinez-Corral, and M. Cho, “Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels,” Opt. Lasers Eng. 111, 114–121 (2018).
[Crossref]

2017 (2)

Y. Yuan, S. Yu, X. Wang, and J. Zhang, “Resolution enhanced 3D image reconstruction by use of ray tracing and auto-focus in computational integral imaging,” Opt. Commun. 404, 73–79 (2017).
[Crossref]

X. Li, M. Zhao, Y. Xing, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Optical encryption via monospectral integral imaging,” Opt. Express 25(25), 31516–31527 (2017).
[Crossref] [PubMed]

2016 (4)

Q. Zhong, Y. Peng, H. Li, and X. Liu, “Optimized image synthesis for multi-projector-type light field display,” J. Disp. Technol. 12(12), 1745–1751 (2016).
[Crossref]

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

J. Kalpana and R. Krishnamoorthi, “Color image retrieval technique with local features based on orthogonal polynomials model and SIFT,” Multimedia Tools Appl. 75(1), 49–69 (2016).
[Crossref]

G. Lv, S. W. Teng, and G. Lu, “Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors,” Pattern Recognit. Lett. 84, 156–162 (2016).
[Crossref]

2015 (5)

S. W. Teng, M. T. Hossain, and G. Lu, “Multimodal image registration technique based on improved local feature descriptors,” J. Electron. Imaging 24(1), 013013 (2015).
[Crossref]

G. A. Montazer and D. Giveki, “Content based image retrieval system using clustered scale invariant feature transforms,” Optik (Stuttg.) 126(18), 1695–1699 (2015).
[Crossref]

S. Luo, W. Mou, K. Althoefer, and H. Liu, “Novel tactile-sift descriptor for object shape recognition,” IEEE Sens. J. 15(9), 5001–5009 (2015).
[Crossref]

Y. Takaki and Y. Yamaguchi, “Flat-panel see-through three-dimensional display based on integral imaging,” Opt. Lett. 40(8), 1873–1876 (2015).
[Crossref] [PubMed]

C. Su, Q. Zhong, Y. Peng, L. Xu, R. Wang, H. Li, and X. Liu, “Grayscale performance enhancement for time-multiplexing light field rendering,” Opt. Express 23(25), 32622–32632 (2015).
[Crossref] [PubMed]

2014 (2)

2013 (3)

Y. Zhu, S. Cheng, V. Stankovic, and L. Stankovic, “Image registration using BP-SIFT,” J. Vis. Commun. Image Represent. 24(4), 448–457 (2013).
[Crossref]

W. L. Zhao and C. W. Ngo, “Flip-invariant SIFT for copy and object detection,” IEEE Trans. Image Process. 22(3), 980–991 (2013).
[Crossref] [PubMed]

X. Xiao, B. Javidi, M. Martinez-Corral, and A. Stern, “Advances in three-dimensional integral imaging: sensing, display, and applications [Invited],” Appl. Opt. 52(4), 546–560 (2013).
[Crossref] [PubMed]

2012 (1)

2011 (4)

2009 (1)

M. Cho and B. Javidi, “Computational reconstruction of three-dimensional integral imaging by rearrangement of elemental image pixels,” J. Disp. Technol. 5(2), 61–65 (2009).
[Crossref]

2007 (1)

2004 (1)

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[Crossref]

2001 (1)

Althoefer, K.

S. Luo, W. Mou, K. Althoefer, and H. Liu, “Novel tactile-sift descriptor for object shape recognition,” IEEE Sens. J. 15(9), 5001–5009 (2015).
[Crossref]

Arimoto, H.

Cha, S.

Chen, N.

Chen, Y.

Cheng, S.

Y. Zhu, S. Cheng, V. Stankovic, and L. Stankovic, “Image registration using BP-SIFT,” J. Vis. Commun. Image Represent. 24(4), 448–457 (2013).
[Crossref]

Cho, B.

B. Cho, P. Kopycki, M. Martinez-Corral, and M. Cho, “Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels,” Opt. Lasers Eng. 111, 114–121 (2018).
[Crossref]

Cho, M.

B. Cho, P. Kopycki, M. Martinez-Corral, and M. Cho, “Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels,” Opt. Lasers Eng. 111, 114–121 (2018).
[Crossref]

K. Inoue, M. Lee, B. Javidi, and M. Cho, “Improved 3D integral imaging reconstruction with elemental image pixel rearrangement,” J. Opt. 20(2), 025703 (2018).
[Crossref]

M. Cho and B. Javidi, “Computational reconstruction of three-dimensional integral imaging by rearrangement of elemental image pixels,” J. Disp. Technol. 5(2), 61–65 (2009).
[Crossref]

Deng, H.

H. Deng, Q. H. Wang, L. Li, and D. H. Li, “An integral-imaging three-dimensional display with wide viewing angle,” J. Soc. Inf. Disp. 19(10), 679–684 (2011).
[Crossref]

Giveki, D.

G. A. Montazer and D. Giveki, “Content based image retrieval system using clustered scale invariant feature transforms,” Optik (Stuttg.) 126(18), 1695–1699 (2015).
[Crossref]

Guo, B.

Guo, T.

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

Hossain, M. T.

S. W. Teng, M. T. Hossain, and G. Lu, “Multimodal image registration technique based on improved local feature descriptors,” J. Electron. Imaging 24(1), 013013 (2015).
[Crossref]

Inoue, K.

K. Inoue, M. Lee, B. Javidi, and M. Cho, “Improved 3D integral imaging reconstruction with elemental image pixel rearrangement,” J. Opt. 20(2), 025703 (2018).
[Crossref]

Jang, J. Y.

Javidi, B.

Jung, J. H.

Kalpana, J.

J. Kalpana and R. Krishnamoorthi, “Color image retrieval technique with local features based on orthogonal polynomials model and SIFT,” Multimedia Tools Appl. 75(1), 49–69 (2016).
[Crossref]

Kim, S. T.

Kopycki, P.

B. Cho, P. Kopycki, M. Martinez-Corral, and M. Cho, “Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels,” Opt. Lasers Eng. 111, 114–121 (2018).
[Crossref]

Krishnamoorthi, R.

J. Kalpana and R. Krishnamoorthi, “Color image retrieval technique with local features based on orthogonal polynomials model and SIFT,” Multimedia Tools Appl. 75(1), 49–69 (2016).
[Crossref]

Lee, B.

Lee, H. S.

Lee, M.

K. Inoue, M. Lee, B. Javidi, and M. Cho, “Improved 3D integral imaging reconstruction with elemental image pixel rearrangement,” J. Opt. 20(2), 025703 (2018).
[Crossref]

Li, D. H.

H. Deng, Q. H. Wang, L. Li, and D. H. Li, “An integral-imaging three-dimensional display with wide viewing angle,” J. Soc. Inf. Disp. 19(10), 679–684 (2011).
[Crossref]

Li, H.

Q. Zhong, Y. Peng, H. Li, and X. Liu, “Optimized image synthesis for multi-projector-type light field display,” J. Disp. Technol. 12(12), 1745–1751 (2016).
[Crossref]

C. Su, Q. Zhong, Y. Peng, L. Xu, R. Wang, H. Li, and X. Liu, “Grayscale performance enhancement for time-multiplexing light field rendering,” Opt. Express 23(25), 32622–32632 (2015).
[Crossref] [PubMed]

Li, L.

Li, W.

Li, X.

Li, Y.

Liu, H.

S. Luo, W. Mou, K. Althoefer, and H. Liu, “Novel tactile-sift descriptor for object shape recognition,” IEEE Sens. J. 15(9), 5001–5009 (2015).
[Crossref]

Liu, X.

Q. Zhong, Y. Peng, H. Li, and X. Liu, “Optimized image synthesis for multi-projector-type light field display,” J. Disp. Technol. 12(12), 1745–1751 (2016).
[Crossref]

C. Su, Q. Zhong, Y. Peng, L. Xu, R. Wang, H. Li, and X. Liu, “Grayscale performance enhancement for time-multiplexing light field rendering,” Opt. Express 23(25), 32622–32632 (2015).
[Crossref] [PubMed]

Liu, Y.

X. Li, Y. Wang, Q. H. Wang, Y. Liu, and X. Zhou, “Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm,” Opt. Lasers Eng. 112, 162–169 (2019).
[Crossref]

Lowe, D. G.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004).
[Crossref]

Lu, G.

G. Lv, S. W. Teng, and G. Lu, “Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors,” Pattern Recognit. Lett. 84, 156–162 (2016).
[Crossref]

S. W. Teng, M. T. Hossain, and G. Lu, “Multimodal image registration technique based on improved local feature descriptors,” J. Electron. Imaging 24(1), 013013 (2015).
[Crossref]

Luo, S.

S. Luo, W. Mou, K. Althoefer, and H. Liu, “Novel tactile-sift descriptor for object shape recognition,” IEEE Sens. J. 15(9), 5001–5009 (2015).
[Crossref]

Lv, G.

G. Lv, S. W. Teng, and G. Lu, “Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors,” Pattern Recognit. Lett. 84, 156–162 (2016).
[Crossref]

Martinez-Corral, M.

B. Cho, P. Kopycki, M. Martinez-Corral, and M. Cho, “Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels,” Opt. Lasers Eng. 111, 114–121 (2018).
[Crossref]

X. Xiao, B. Javidi, M. Martinez-Corral, and A. Stern, “Advances in three-dimensional integral imaging: sensing, display, and applications [Invited],” Appl. Opt. 52(4), 546–560 (2013).
[Crossref] [PubMed]

Montazer, G. A.

G. A. Montazer and D. Giveki, “Content based image retrieval system using clustered scale invariant feature transforms,” Optik (Stuttg.) 126(18), 1695–1699 (2015).
[Crossref]

Mou, W.

S. Luo, W. Mou, K. Althoefer, and H. Liu, “Novel tactile-sift descriptor for object shape recognition,” IEEE Sens. J. 15(9), 5001–5009 (2015).
[Crossref]

Ngo, C. W.

W. L. Zhao and C. W. Ngo, “Flip-invariant SIFT for copy and object detection,” IEEE Trans. Image Process. 22(3), 980–991 (2013).
[Crossref] [PubMed]

Park, J. H.

Peng, R.

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

Peng, Y.

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

Q. Zhong, Y. Peng, H. Li, and X. Liu, “Optimized image synthesis for multi-projector-type light field display,” J. Disp. Technol. 12(12), 1745–1751 (2016).
[Crossref]

C. Su, Q. Zhong, Y. Peng, L. Xu, R. Wang, H. Li, and X. Liu, “Grayscale performance enhancement for time-multiplexing light field rendering,” Opt. Express 23(25), 32622–32632 (2015).
[Crossref] [PubMed]

Shin, D. H.

Shin, S. H.

Stankovic, L.

Y. Zhu, S. Cheng, V. Stankovic, and L. Stankovic, “Image registration using BP-SIFT,” J. Vis. Commun. Image Represent. 24(4), 448–457 (2013).
[Crossref]

Stankovic, V.

Y. Zhu, S. Cheng, V. Stankovic, and L. Stankovic, “Image registration using BP-SIFT,” J. Vis. Commun. Image Represent. 24(4), 448–457 (2013).
[Crossref]

Stern, A.

Su, C.

Takaki, Y.

Teng, S. W.

G. Lv, S. W. Teng, and G. Lu, “Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors,” Pattern Recognit. Lett. 84, 156–162 (2016).
[Crossref]

S. W. Teng, M. T. Hossain, and G. Lu, “Multimodal image registration technique based on improved local feature descriptors,” J. Electron. Imaging 24(1), 013013 (2015).
[Crossref]

Wang, Q. H.

X. Li, Y. Wang, Q. H. Wang, Y. Liu, and X. Zhou, “Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm,” Opt. Lasers Eng. 112, 162–169 (2019).
[Crossref]

X. Li, M. Zhao, Y. Xing, H. L. Zhang, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Designing optical 3d images encryption and reconstruction using monospectral synthetic aperture integral imaging,” Opt. Express 26(9), 11084–11099 (2018).
[Crossref] [PubMed]

X. Li, M. Zhao, Y. Xing, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Optical encryption via monospectral integral imaging,” Opt. Express 25(25), 31516–31527 (2017).
[Crossref] [PubMed]

H. Deng, Q. H. Wang, L. Li, and D. H. Li, “An integral-imaging three-dimensional display with wide viewing angle,” J. Soc. Inf. Disp. 19(10), 679–684 (2011).
[Crossref]

Wang, R.

Wang, X.

Y. Yuan, S. Yu, X. Wang, and J. Zhang, “Resolution enhanced 3D image reconstruction by use of ray tracing and auto-focus in computational integral imaging,” Opt. Commun. 404, 73–79 (2017).
[Crossref]

Y. Chen, X. Wang, J. Zhang, S. Yu, Q. Zhang, and B. Guo, “Resolution improvement of integral imaging based on time multiplexing sub-pixel coding method on common display panel,” Opt. Express 22(15), 17897–17907 (2014).
[Crossref] [PubMed]

Wang, Y.

X. Li, Y. Wang, Q. H. Wang, Y. Liu, and X. Zhou, “Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm,” Opt. Lasers Eng. 112, 162–169 (2019).
[Crossref]

Xiao, X.

Xing, Y.

Xiong, J.

J. Yu, F. Zhang, and J. Xiong, “An innovative sift-based method for rigid video object recognition,” Math. Probl. Eng. 2014, 138927 (2014).
[Crossref]

Xu, L.

Yamaguchi, Y.

Yeom, J.

Yoo, H.

Yu, J.

J. Yu, F. Zhang, and J. Xiong, “An innovative sift-based method for rigid video object recognition,” Math. Probl. Eng. 2014, 138927 (2014).
[Crossref]

Yu, S.

Y. Yuan, S. Yu, X. Wang, and J. Zhang, “Resolution enhanced 3D image reconstruction by use of ray tracing and auto-focus in computational integral imaging,” Opt. Commun. 404, 73–79 (2017).
[Crossref]

Y. Chen, X. Wang, J. Zhang, S. Yu, Q. Zhang, and B. Guo, “Resolution improvement of integral imaging based on time multiplexing sub-pixel coding method on common display panel,” Opt. Express 22(15), 17897–17907 (2014).
[Crossref] [PubMed]

Yuan, Y.

Y. Yuan, S. Yu, X. Wang, and J. Zhang, “Resolution enhanced 3D image reconstruction by use of ray tracing and auto-focus in computational integral imaging,” Opt. Commun. 404, 73–79 (2017).
[Crossref]

Zeng, X.

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

Zhang, F.

J. Yu, F. Zhang, and J. Xiong, “An innovative sift-based method for rigid video object recognition,” Math. Probl. Eng. 2014, 138927 (2014).
[Crossref]

Zhang, H. L.

Zhang, J.

Y. Yuan, S. Yu, X. Wang, and J. Zhang, “Resolution enhanced 3D image reconstruction by use of ray tracing and auto-focus in computational integral imaging,” Opt. Commun. 404, 73–79 (2017).
[Crossref]

Y. Chen, X. Wang, J. Zhang, S. Yu, Q. Zhang, and B. Guo, “Resolution improvement of integral imaging based on time multiplexing sub-pixel coding method on common display panel,” Opt. Express 22(15), 17897–17907 (2014).
[Crossref] [PubMed]

Zhang, Q.

Zhang, Y. A.

X. Zhou, Y. Peng, R. Peng, X. Zeng, Y. A. Zhang, and T. Guo, “Fabrication of large-scale microlens arrays based on screen printing for integral imaging 3D display,” ACS Appl. Mater. Interfaces 8(36), 24248–24255 (2016).
[Crossref] [PubMed]

Zhao, M.

Zhao, W. L.

W. L. Zhao and C. W. Ngo, “Flip-invariant SIFT for copy and object detection,” IEEE Trans. Image Process. 22(3), 980–991 (2013).
[Crossref] [PubMed]

Zhong, Q.

Q. Zhong, Y. Peng, H. Li, and X. Liu, “Optimized image synthesis for multi-projector-type light field display,” J. Disp. Technol. 12(12), 1745–1751 (2016).
[Crossref]

C. Su, Q. Zhong, Y. Peng, L. Xu, R. Wang, H. Li, and X. Liu, “Grayscale performance enhancement for time-multiplexing light field rendering,” Opt. Express 23(25), 32622–32632 (2015).
[Crossref] [PubMed]

Zhou, X.

X. Li, Y. Wang, Q. H. Wang, Y. Liu, and X. Zhou, “Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm,” Opt. Lasers Eng. 112, 162–169 (2019).
[Crossref]

X. Li, M. Zhao, Y. Xing, H. L. Zhang, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Designing optical 3d images encryption and reconstruction using monospectral synthetic aperture integral imaging,” Opt. Express 26(9), 11084–11099 (2018).
[Crossref] [PubMed]

X. Li, M. Zhao, Y. Xing, L. Li, S. T. Kim, X. Zhou, and Q. H. Wang, “Optical encryption via monospectral integral imaging,” Opt. Express 25(25), 31516–31527 (2017).
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Figures (13)

Fig. 1
Fig. 1 Block diagram of the proposed CIIR method.
Fig. 2
Fig. 2 Schematic diagram of integral imaging.
Fig. 3
Fig. 3 Schematic of conventional method for CIIR.
Fig. 4
Fig. 4 Schematic of improved non-periodic pixels extracting method for CIIR.
Fig. 5
Fig. 5 Moving direction of the patch from template image.
Fig. 6
Fig. 6 Object and elemental images used in simulation: (a) object (b) elemental images. Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 7
Fig. 7 Reconstructed results of left view, front view, and right view using the: (a) conventional CIIR method [30]; (b) CIIR method based on ray tracing and auto-focus [18]; (c) improved non-periodic pixels extracting CIIR method; (d) proposed CIIR method (Method 1); (e) proposed CIIR method (Method 2). Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 8
Fig. 8 Setups of experimental systems: (a) System 1 (b) System 2. Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 9
Fig. 9 Elemental images used in experiments: (a) System 1 (b) System 2.
Fig. 10
Fig. 10 Reconstructed results of left view, front view, and right view of System 1 using: (a) conventional CIIR method [30]; (b) CIIR method based on ray tracing and auto-focus [18]; (c) improved non-periodic pixels extracting CIIR method; (d) proposed CIIR method (Method 1); (e) proposed CIIR method (Method 2). Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 11
Fig. 11 Reconstructed results of left view, front view, and right view of System 2 using: (a) conventional CIIR method [30]; (b) CIIR method based on ray tracing and auto-focus [18]; (c) improved non-periodic pixels extracting CIIR method; (d) proposed CIIR method (Method 1); (e) proposed CIIR method (Method 2). Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 12
Fig. 12 Reconstructed results using the proposed CIIR method of System 1 when the view point is at (25, 25, 4050) and the values of PSNR in the process of distorted region searching are 20, 25, and 30: (a) using the proposed Method 1; (b) using the proposed Method 2. Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com
Fig. 13
Fig. 13 Reconstructed results using the proposed CIIR method of System 2 when the view point is at (27, 27, 580) and the values of PSNR in the process of distorted region searching are 20, 25, and 30: (a) using the proposed Method 1; (b) using the proposed Method 2. Rubik’s Cube used by permission Rubik’s Brand Ltd. www.rubik’s.com

Tables (9)

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Table 1 Parameters used in the computational reconstruction of simulation

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Table 2 Number of the distorted patches, size of the compressed image patches via JPEG (in Method 1), and size of the compressed location of the good regions in elemental images (in Method 2)

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Table 3 PSNR and SSIM of the reconstructed results at different view points

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Table 4 Parameters used in the computational reconstruction of experiments (System 1)

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Table 5 Parameters used in the computational reconstruction of experiments (System 2)

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Table 6 Number of distorted patches, size of the compressed image patches via JPEG (in Method 1), and size of the compressed location of the good regions in elemental images (in Method 2)

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Table 7 PSNR and SSIM of the reconstructed results at different view points

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Table 8 Number of distorted patches, size of the compressed image patches via JPEG (in Method 1), and size of the compressed location of the good regions in elemental images (in Method 2)

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Table 9 PSNR and SSIM of the reconstructed results at different view points

Equations (5)

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f ( r , t ) = ψ ( k , v ) exp [ j 2 π ( k r + v t ) ] d k d v ,
f ( x , y , z ; λ ; t ) = ϕ ( α λ , β λ ; t ) exp [ j 2 π ( α λ x + β λ y ) ] d ( α λ ) d ( β λ ) .
f a b ( x , y , z ; λ ; t ) = b =1 B a = 1 A ϕ a b ( α a b λ , β a b λ ; t ) exp ( j 2 π λ 1 α a b 2 β a b 2 z ) exp [ j 2 π ( α a b λ x + β a b λ y ) ] ,
1 f = 1 g + 1 l 1 .
c max = g l 2 P L R E I l 1 ( l 1 + l 2 + g ) P E I , ( l 2 g ) .

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