L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis Mach. Intell. 39, 2481–2495 (2017).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), pp. 234–241.

J. Vargas, C. Sorzano, J. Estrada, and J. Carazo, “Generalization of the principal component analysis algorithm for interferometry,” Opt. Commun. 286, 130–134 (2013).

[Crossref]

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2017), pp. 1251–1258.

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis Mach. Intell. 39, 2481–2495 (2017).

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

G. Dardikman and N. T. Shaked, “Phase unwrapping using residual neural networks,” in Computational Optical Sensing and Imaging, (Optical Society of America, 2018), pp. CW3B–5.

[Crossref]

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 2961–2969.

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

J. Vargas, C. Sorzano, J. Estrada, and J. Carazo, “Generalization of the principal component analysis algorithm for interferometry,” Opt. Commun. 286, 130–134 (2013).

[Crossref]

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), pp. 234–241.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

C. Prati, M. Giani, and N. Leuratti, “SAR interferometry: A 2-D phase unwrapping technique based on phase and absolute values informations,” in Proceedings of IEEE Conference on International Geoscience and Remote Sensing Symposium, (IEEE, 1990), pp. 2043–2046.

[Crossref]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, (IEEE, 2015), pp. 91–99.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 2961–2969.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 2961–2969.

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: Two-dimensional phase unwrapping,” Radio Sci. 23, 713–720 (1988).

[Crossref]

G. E. Spoorthi, S. Gorthi, and R. K. S. S. Gorthi, “Phasenet: A deep convolutional neural network for two-dimensional phase unwrapping,” IEEE Signal Process. Lett. 26, 54–58 (2019).

[Crossref]

G. E. Spoorthi, S. Gorthi, and R. K. S. S. Gorthi, “Phasenet: A deep convolutional neural network for two-dimensional phase unwrapping,” IEEE Signal Process. Lett. 26, 54–58 (2019).

[Crossref]

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, (IEEE, 2015), pp. 91–99.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 2961–2969.

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 (2015).

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis Mach. Intell. 39, 2481–2495 (2017).

[Crossref]

C. Prati, M. Giani, and N. Leuratti, “SAR interferometry: A 2-D phase unwrapping technique based on phase and absolute values informations,” in Proceedings of IEEE Conference on International Geoscience and Remote Sensing Symposium, (IEEE, 1990), pp. 2043–2046.

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

C. Prati, M. Giani, and N. Leuratti, “SAR interferometry: A 2-D phase unwrapping technique based on phase and absolute values informations,” in Proceedings of IEEE Conference on International Geoscience and Remote Sensing Symposium, (IEEE, 1990), pp. 2043–2046.

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, (IEEE, 2015), pp. 91–99.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), pp. 234–241.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

G. Dardikman and N. T. Shaked, “Phase unwrapping using residual neural networks,” in Computational Optical Sensing and Imaging, (Optical Society of America, 2018), pp. CW3B–5.

[Crossref]

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

J. Vargas, C. Sorzano, J. Estrada, and J. Carazo, “Generalization of the principal component analysis algorithm for interferometry,” Opt. Commun. 286, 130–134 (2013).

[Crossref]

G. E. Spoorthi, S. Gorthi, and R. K. S. S. Gorthi, “Phasenet: A deep convolutional neural network for two-dimensional phase unwrapping,” IEEE Signal Process. Lett. 26, 54–58 (2019).

[Crossref]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, (IEEE, 2015), pp. 91–99.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 (2015).

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

J. Vargas, C. Sorzano, J. Estrada, and J. Carazo, “Generalization of the principal component analysis algorithm for interferometry,” Opt. Commun. 286, 130–134 (2013).

[Crossref]

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: Two-dimensional phase unwrapping,” Radio Sci. 23, 713–720 (1988).

[Crossref]

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: Two-dimensional phase unwrapping,” Radio Sci. 23, 713–720 (1988).

[Crossref]

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

C. Zuo, “Connections between transport of intensity equation and two-dimensional phase unwrapping,” arXiv preprint arXiv:1704.03950 (2017).

S. Feng, C. Qian, G. Gu, T. Tao, Z. Liang, H. Yan, Y. Wei, and Z. Chao, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(2), 025001 (2019).

[Crossref]

M. A. Herráez, D. R. Burton, M. J. Lalor, and M. A. Gdeisat, “Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path,” Appl. Opt. 41, 7437–7444 (2002).

[Crossref]
[PubMed]

J. Martinez-Carranza, K. Falaggis, and T. Kozacki, “Fast and accurate phase-unwrapping algorithm based on the transport of intensity equation,” Appl. Opt. 56, 7079–7088 (2017).

[Crossref]
[PubMed]

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[Crossref]
[PubMed]

D. J. Bone, “Fourier fringe analysis: the two-dimensional phase unwrapping problem,” Appl. Opt. 30, 3627–3632 (1991).

[Crossref]
[PubMed]

G. E. Spoorthi, S. Gorthi, and R. K. S. S. Gorthi, “Phasenet: A deep convolutional neural network for two-dimensional phase unwrapping,” IEEE Signal Process. Lett. 26, 54–58 (2019).

[Crossref]

V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis Mach. Intell. 39, 2481–2495 (2017).

[Crossref]

Z. Zhao, H. Zhang, Z. Xiao, H. Du, Y. Zhuang, C. Fan, and H. Zhao, “Robust 2D phase unwrapping algorithm based on the transport of intensity equation,” Meas. Sci. Technol. 30, 015201 (2018).

[Crossref]

J. Vargas, C. Sorzano, J. Estrada, and J. Carazo, “Generalization of the principal component analysis algorithm for interferometry,” Opt. Commun. 286, 130–134 (2013).

[Crossref]

R. M. Goldstein, H. A. Zebker, and C. L. Werner, “Satellite radar interferometry: Two-dimensional phase unwrapping,” Radio Sci. 23, 713–720 (1988).

[Crossref]

M. F. Kasim, “Fast 2D phase unwrapping implementation in MATLAB,” https://github.com/mfkasim91/unwrap_phase/ .

C. Prati, M. Giani, and N. Leuratti, “SAR interferometry: A 2-D phase unwrapping technique based on phase and absolute values informations,” in Proceedings of IEEE Conference on International Geoscience and Remote Sensing Symposium, (IEEE, 1990), pp. 2043–2046.

[Crossref]

C. Zuo, “Connections between transport of intensity equation and two-dimensional phase unwrapping,” arXiv preprint arXiv:1704.03950 (2017).

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in Neural Information Processing Systems, (IEEE, 2015), pp. 91–99.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision, (Springer, 2016), pp. 21–37.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2017), pp. 1251–1258.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (IEEE, 2015), pp. 3431–3440.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, (Springer, 2015), pp. 234–241.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision, (Springer, 2018), pp. 801–818.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 2961–2969.

J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE International Conference on Computer Vision, (IEEE, 2017), pp. 764–773.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167 (2015).

W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik, “Two-dimensional phase unwrapping using neural networks,” in Proceedings of IEEE Conference on Image Analysis and Interpretation, (IEEE, 2000), pp. 274–277.

G. Dardikman and N. T. Shaked, “Phase unwrapping using residual neural networks,” in Computational Optical Sensing and Imaging, (Optical Society of America, 2018), pp. CW3B–5.

[Crossref]