A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

D. Kingma and J. Ba, “Adam: a method for stochastic optimization,” in 3rd International Conference on Learning Representations, San Diego, California (2014).

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

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

Q. Li, C. Bao, J. Zhao, and Z. Jiang, “A new fast quality-guided flood-fill phase unwrapping algorithm,” J. Phys. Conf. Ser. 1069, 012182 (2018).

[Crossref]

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

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

T. R. Judge and P. Bryanston-Cross, “A review of phase unwrapping techniques in fringe analysis,” Opt. Lasers Eng. 21, 199–239 (1994).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

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

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).

[Crossref]

K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao, “Y-Net: a one-to-two deep learning framework for digital holographic reconstruction,” Opt. Lett. 44, 4765–4768 (2019).

[Crossref]

S. V. D. Jeught, J. Sijbers, and J. J. Dirckx, “Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications,” J. Imaging 1, 31–44 (2015).

[Crossref]

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

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

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

G. Spoorthi, R. K. S. S. Gorthi, and S. Gorthi, “PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach,” IEEE Trans. Image Process. 29, 4862–4872 (2020).

[Crossref]

G. 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 (2018).

[Crossref]

V. K. Sumanth and R. K. S. S. Gorthi, “A deep learning framework for 3D surface profiling of the objects using digital holographic interferometry,” in IEEE International Conference on Image Processing (ICIP) (IEEE, 2020), pp. 2656–2660.

R. G. Waghmare, R. S. S. Gorthi, and D. Mishra, “Wrapped statistics-based phase retrieval from interference fringes,” J. Mod. Opt. 63, 1384–1390 (2016).

[Crossref]

G. Spoorthi, R. K. S. S. Gorthi, and S. Gorthi, “PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach,” IEEE Trans. Image Process. 29, 4862–4872 (2020).

[Crossref]

G. 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 (2018).

[Crossref]

R. G. Waghmare, D. Mishra, G. S. Subrahmanyam, E. Banoth, and S. S. Gorthi, “Signal tracking approach for phase estimation in digital holographic interferometry,” Appl. Opt. 53, 4150–4157 (2014).

[Crossref]

S. S. Gorthi, G. Rajshekhar, and P. Rastogi, “Strain estimation in digital holographic interferometry using piecewise polynomial phase approximation based method,” Opt. Express 18, 560–565 (2010).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (PMLR, 2015), pp. 448–456.

S. V. D. Jeught, J. Sijbers, and J. J. Dirckx, “Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications,” J. Imaging 1, 31–44 (2015).

[Crossref]

Q. Li, C. Bao, J. Zhao, and Z. Jiang, “A new fast quality-guided flood-fill phase unwrapping algorithm,” J. Phys. Conf. Ser. 1069, 012182 (2018).

[Crossref]

T. R. Judge and P. Bryanston-Cross, “A review of phase unwrapping techniques in fringe analysis,” Opt. Lasers Eng. 21, 199–239 (1994).

[Crossref]

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).

[Crossref]

K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao, “Y-Net: a one-to-two deep learning framework for digital holographic reconstruction,” Opt. Lett. 44, 4765–4768 (2019).

[Crossref]

M. Zhao, L. Huang, Q. Zhang, X. Su, A. Asundi, and Q. Kemao, “Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies,” Appl. Opt. 50, 6214–6224 (2011).

[Crossref]

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

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

D. Kingma and J. Ba, “Adam: a method for stochastic optimization,” in 3rd International Conference on Learning Representations, San Diego, California (2014).

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

Z. Ren, Z. Xu, and E. Y. Lam, “End-to-end deep learning framework for digital holographic reconstruction,” Adv. Photon. 1, 016004 (2019).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

Q. Li, C. Bao, J. Zhao, and Z. Jiang, “A new fast quality-guided flood-fill phase unwrapping algorithm,” J. Phys. Conf. Ser. 1069, 012182 (2018).

[Crossref]

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: designing skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imaging 39, 1856–1867 (2019).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

F. Lovergine, S. Stramaglia, G. Nico, and N. Veneziani, “Fast weighted least squares for solving the phase unwrapping problem,” in IEEE International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293) (IEEE, 1999), Vol. 2, pp. 1348–1350.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

R. G. Waghmare, R. S. S. Gorthi, and D. Mishra, “Wrapped statistics-based phase retrieval from interference fringes,” J. Mod. Opt. 63, 1384–1390 (2016).

[Crossref]

R. G. Waghmare, P. R. Sukumar, G. R. K. S. Subrahmanyam, R. K. Singh, and D. Mishra, “Particle-filter-based phase estimation in digital holographic interferometry,” J. Opt. Soc. Am. A 33, 326–332 (2016).

[Crossref]

R. G. Waghmare, D. Mishra, G. S. Subrahmanyam, E. Banoth, and S. S. Gorthi, “Signal tracking approach for phase estimation in digital holographic interferometry,” Appl. Opt. 53, 4150–4157 (2014).

[Crossref]

F. Lovergine, S. Stramaglia, G. Nico, and N. Veneziani, “Fast weighted least squares for solving the phase unwrapping problem,” in IEEE International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293) (IEEE, 1999), Vol. 2, pp. 1348–1350.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

Z. Ren, Z. Xu, and E. Y. Lam, “End-to-end deep learning framework for digital holographic reconstruction,” Adv. Photon. 1, 016004 (2019).

[Crossref]

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

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: designing skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imaging 39, 1856–1867 (2019).

[Crossref]

S. V. D. Jeught, J. Sijbers, and J. J. Dirckx, “Fast Fourier-based phase unwrapping on the graphics processing unit in real-time imaging applications,” J. Imaging 1, 31–44 (2015).

[Crossref]

G. Spoorthi, R. K. S. S. Gorthi, and S. Gorthi, “PhaseNet 2.0: phase unwrapping of noisy data based on deep learning approach,” IEEE Trans. Image Process. 29, 4862–4872 (2020).

[Crossref]

G. 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 (2018).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

F. Lovergine, S. Stramaglia, G. Nico, and N. Veneziani, “Fast weighted least squares for solving the phase unwrapping problem,” in IEEE International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293) (IEEE, 1999), Vol. 2, pp. 1348–1350.

V. K. Sumanth and R. K. S. S. Gorthi, “A deep learning framework for 3D surface profiling of the objects using digital holographic interferometry,” in IEEE International Conference on Image Processing (ICIP) (IEEE, 2020), pp. 2656–2660.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (PMLR, 2015), pp. 448–456.

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: designing skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imaging 39, 1856–1867 (2019).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

F. Lovergine, S. Stramaglia, G. Nico, and N. Veneziani, “Fast weighted least squares for solving the phase unwrapping problem,” in IEEE International Geoscience and Remote Sensing Symposium. IGARSS’99 (Cat. No. 99CH36293) (IEEE, 1999), Vol. 2, pp. 1348–1350.

R. G. Waghmare, P. R. Sukumar, G. R. K. S. Subrahmanyam, R. K. Singh, and D. Mishra, “Particle-filter-based phase estimation in digital holographic interferometry,” J. Opt. Soc. Am. A 33, 326–332 (2016).

[Crossref]

R. G. Waghmare, R. S. S. Gorthi, and D. Mishra, “Wrapped statistics-based phase retrieval from interference fringes,” J. Mod. Opt. 63, 1384–1390 (2016).

[Crossref]

R. G. Waghmare, D. Mishra, G. S. Subrahmanyam, E. Banoth, and S. S. Gorthi, “Signal tracking approach for phase estimation in digital holographic interferometry,” Appl. Opt. 53, 4150–4157 (2014).

[Crossref]

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).

[Crossref]

K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao, “Y-Net: a one-to-two deep learning framework for digital holographic reconstruction,” Opt. Lett. 44, 4765–4768 (2019).

[Crossref]

S. Mehta, E. Mercan, J. Bartlett, D. Weaver, J. G. Elmore, and L. Shapiro, “Y-Net: joint segmentation and classification for diagnosis of breast biopsy images,” in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, 2018), pp. 893–901.

Z. Ren, Z. Xu, and E. Y. Lam, “End-to-end deep learning framework for digital holographic reconstruction,” Adv. Photon. 1, 016004 (2019).

[Crossref]

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (2019), pp. 8026–8037.

K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao, “One-step robust deep learning phase unwrapping,” Opt. Express 27, 15100–15115 (2019).

[Crossref]

K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao, “Y-Net: a one-to-two deep learning framework for digital holographic reconstruction,” Opt. Lett. 44, 4765–4768 (2019).

[Crossref]

Q. Li, C. Bao, J. Zhao, and Z. Jiang, “A new fast quality-guided flood-fill phase unwrapping algorithm,” J. Phys. Conf. Ser. 1069, 012182 (2018).

[Crossref]

Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: designing skip connections to exploit multiscale features in image segmentation,” IEEE Trans. Med. Imaging 39, 1856–1867 (2019).

[Crossref]