J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

Y. Aron and Y. Gronau, “Polarization in the lwir: a method to improve target aquisition,” in Infrared Technology and Applications XXXI, vol. 5783 (International Society for Optics and Photonics, 2005), pp. 653–662.

[Crossref]

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing 13, 600–612 (2004).

[Crossref]
[PubMed]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Springer International Publishing, Cham, 2015), pp. 234–241.

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, B. Hui, and Z. Chang, “Image interpolation for division of focal plane polarimeters with intensity correlation,” Opt. Express 24, 20799–20807 (2016).

[Crossref]
[PubMed]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).

[Crossref]

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Springer International Publishing, Cham, 2015), pp. 234–241.

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for neural networks for image processing,” arXiv preprint arXiv:1511.08861 (2015).

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for neural networks for image processing,” arXiv preprint arXiv:1511.08861 (2015).

D. H. Goldstein, “Polarimetric characterization of federal standard paints,” in Polarization Analysis, Measurement, and Remote Sensing III, vol. 4133 (International Society for Optics and Photonics, 2000), pp. 112–124.

[Crossref]

Y. Aron and Y. Gronau, “Polarization in the lwir: a method to improve target aquisition,” in Infrared Technology and Applications XXXI, vol. 5783 (International Society for Optics and Photonics, 2005), pp. 653–662.

[Crossref]

S. Gao and V. Gruev, “Bilinear and bicubic interpolation methods for division of focal plane polarimeters,” Opt. Express 19, 26161–26173 (2011).

[Crossref]

R. Perkins and V. Gruev, “Signal-to-noise analysis of stokes parameters in division of focal plane polarimeters,” Opt. Express 18, 25815–25824 (2010).

[Crossref]
[PubMed]

S. Gao and V. Gruev, “Gradient based interpolation for division of focal plane polarization imaging sensors,” in 2012 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2012), pp. 1855–1858.

[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).

[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 1026–1034.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), (2010), pp. 807–814.

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, B. Hui, and Z. Chang, “Image interpolation for division of focal plane polarimeters with intensity correlation,” Opt. Express 24, 20799–20807 (2016).

[Crossref]
[PubMed]

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for neural networks for image processing,” arXiv preprint arXiv:1511.08861 (2015).

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).

[Crossref]

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, B. Hui, and Z. Chang, “Image interpolation for division of focal plane polarimeters with intensity correlation,” Opt. Express 24, 20799–20807 (2016).

[Crossref]
[PubMed]

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), (2010), pp. 807–814.

E. Salomatina-Motts, V. Neel, and A. Yaroslavskaya, “Multimodal polarization system for imaging skin cancer,” Opt. Spectrosc. 107, 884–890 (2009).

[Crossref]

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 1026–1034.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Springer International Publishing, Cham, 2015), pp. 234–241.

E. Salomatina-Motts, V. Neel, and A. Yaroslavskaya, “Multimodal polarization system for imaging skin cancer,” Opt. Spectrosc. 107, 884–890 (2009).

[Crossref]

M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof, and A. Theuwissen, “Integrated polarization analyzing cmos image sensor for material classification,” IEEE Sensors J. 11, 1692–1703 (2011).

[Crossref]

M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof, and A. Theuwissen, “Integrated polarization analyzing cmos image sensor for material classification,” IEEE Sensors J. 11, 1692–1703 (2011).

[Crossref]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing 13, 600–612 (2004).

[Crossref]
[PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing 13, 600–612 (2004).

[Crossref]
[PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 1026–1034.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).

[Crossref]

M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof, and A. Theuwissen, “Integrated polarization analyzing cmos image sensor for material classification,” IEEE Sensors J. 11, 1692–1703 (2011).

[Crossref]

M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof, and A. Theuwissen, “Integrated polarization analyzing cmos image sensor for material classification,” IEEE Sensors J. 11, 1692–1703 (2011).

[Crossref]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing 13, 600–612 (2004).

[Crossref]
[PubMed]

E. Salomatina-Motts, V. Neel, and A. Yaroslavskaya, “Multimodal polarization system for imaging skin cancer,” Opt. Spectrosc. 107, 884–890 (2009).

[Crossref]

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, H. Luo, B. Hui, and Z. Chang, “Image interpolation for division of focal plane polarimeters with intensity correlation,” Opt. Express 24, 20799–20807 (2016).

[Crossref]
[PubMed]

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 1026–1034.

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for neural networks for image processing,” arXiv preprint arXiv:1511.08861 (2015).

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof, and A. Theuwissen, “Integrated polarization analyzing cmos image sensor for material classification,” IEEE Sensors J. 11, 1692–1703 (2011).

[Crossref]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing 13, 600–612 (2004).

[Crossref]
[PubMed]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38, 295–307 (2016).

[Crossref]

R. Perkins and V. Gruev, “Signal-to-noise analysis of stokes parameters in division of focal plane polarimeters,” Opt. Express 18, 25815–25824 (2010).

[Crossref]
[PubMed]

S. Gao and V. Gruev, “Bilinear and bicubic interpolation methods for division of focal plane polarimeters,” Opt. Express 19, 26161–26173 (2011).

[Crossref]

B. Huang, T. Liu, H. Hu, J. Han, and M. Yu, “Underwater image recovery considering polarization effects of objects,” Opt. Express 24, 9826–9838 (2016).

[Crossref]
[PubMed]

J. Zhang, H. Luo, B. Hui, and Z. Chang, “Image interpolation for division of focal plane polarimeters with intensity correlation,” Opt. Express 24, 20799–20807 (2016).

[Crossref]
[PubMed]

J. Zhang, H. Luo, R. Liang, A. Ahmed, X. Zhang, B. Hui, and Z. Chang, “Sparse representation-based demosaicing method for microgrid polarimeter imagery,” Opt. Lett. 43, 3265–3268 (2018).

[Crossref]
[PubMed]

J. Zhang, J. Shao, H. Luo, X. Zhang, B. Hui, Z. Chang, and R. Liang, “Learning a convolutional demosaicing network for microgrid polarimeter imagery,” Opt. Lett. 43, 4534–4537 (2018).

[Crossref]
[PubMed]

E. Salomatina-Motts, V. Neel, and A. Yaroslavskaya, “Multimodal polarization system for imaging skin cancer,” Opt. Spectrosc. 107, 884–890 (2009).

[Crossref]

S. Gao and V. Gruev, “Gradient based interpolation for division of focal plane polarization imaging sensors,” in 2012 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2012), pp. 1855–1858.

[Crossref]

J. Zhang, W. Ye, A. Ahmed, Z. Qiu, Y. Cao, and X. Zhao, “A novel smoothness-based interpolation algorithm for division of focal plane polarimeters,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), (IEEE, 2017), pp. 1–4.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Springer International Publishing, Cham, 2015), pp. 234–241.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10), (2010), pp. 807–814.

D. H. Goldstein, “Polarimetric characterization of federal standard paints,” in Polarization Analysis, Measurement, and Remote Sensing III, vol. 4133 (International Society for Optics and Photonics, 2000), pp. 112–124.

[Crossref]

Y. Aron and Y. Gronau, “Polarization in the lwir: a method to improve target aquisition,” in Infrared Technology and Applications XXXI, vol. 5783 (International Society for Optics and Photonics, 2005), pp. 653–662.

[Crossref]

H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for neural networks for image processing,” arXiv preprint arXiv:1511.08861 (2015).

K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in The IEEE International Conference on Computer Vision (ICCV), (2015), pp. 1026–1034.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).