E. Zisselman, A. Adler, and M. Elad, “Compressed learning for image classification: A deep neural network approach,” Process. Anal. Learn. Images, Shapes, Forms 19, 3–17 (2018).

[Crossref]

L. Galvis, D. Lau, X. Ma, H. Arguello, and G. R. Arce, “Coded aperture design in compressive spectral imaging based on side information,” Appl. Opt. 56(22), 6332–6340 (2017).

[Crossref]

L. Galvis, H. Arguello, and G. R. Arce, “Coded aperture design in mismatched compressive spectral imaging,” Appl. Opt. 54(33), 9875–9882 (2015).

[Crossref]

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

H. Arguello, H. Rueda, Y. Wu, D. W. Prather, and G. R. Arce, “Higher-order computational model for coded aperture spectral imaging,” Appl. Opt. 52(10), D12–D21 (2013).

[Crossref]

J. Bacca, C. V. Correa, and H. Arguello, “Noniterative hyperspectral image reconstruction from compressive fused measurements,” IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 12(4), 1231–1239 (2019).

[Crossref]

C. Hinojosa, J. Bacca, and H. Arguello, “Coded aperture design for compressive spectral subspace clustering,” IEEE J. Sel. Top. Signal Process. 12(6), 1589–1600 (2018).

[Crossref]

H. Garcia, C. V. Correa, and H. Arguello, “Multi-resolution compressive spectral imaging reconstruction from single pixel measurements,” IEEE Transactions on Image Process. 27(12), 6174–6184 (2018).

[Crossref]

L. Galvis, D. Lau, X. Ma, H. Arguello, and G. R. Arce, “Coded aperture design in compressive spectral imaging based on side information,” Appl. Opt. 56(22), 6332–6340 (2017).

[Crossref]

L. Galvis, H. Arguello, and G. R. Arce, “Coded aperture design in mismatched compressive spectral imaging,” Appl. Opt. 54(33), 9875–9882 (2015).

[Crossref]

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

H. Arguello, H. Rueda, Y. Wu, D. W. Prather, and G. R. Arce, “Higher-order computational model for coded aperture spectral imaging,” Appl. Opt. 52(10), D12–D21 (2013).

[Crossref]

J. Bacca, H. Vargas-García, D. Molina-Velasco, and H. Arguello, “Single pixel compressive spectral polarization imaging using a movable micro-polarizer array,” Revista Fac. de Ing. Universidad de Antioquia pp. 91–99 (2018).

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, (2015), pp. 1–15.

J. Bacca, C. V. Correa, and H. Arguello, “Noniterative hyperspectral image reconstruction from compressive fused measurements,” IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 12(4), 1231–1239 (2019).

[Crossref]

C. Hinojosa, J. Bacca, and H. Arguello, “Coded aperture design for compressive spectral subspace clustering,” IEEE J. Sel. Top. Signal Process. 12(6), 1589–1600 (2018).

[Crossref]

J. Bacca, H. Vargas-García, D. Molina-Velasco, and H. Arguello, “Single pixel compressive spectral polarization imaging using a movable micro-polarizer array,” Revista Fac. de Ing. Universidad de Antioquia pp. 91–99 (2018).

M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Top. Signal Process. 4(2), 445–460 (2010).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

A. Mousavi and R. G. Baraniuk, “Learning to invert: Signal recovery via deep convolutional networks,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2017), pp. 2272–2276.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).

[Crossref]

S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation 4(1), 1–58 (1992).

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).

[Crossref]

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010, (Springer, 2010), pp. 177–186.

M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Top. Signal Process. 4(2), 445–460 (2010).

[Crossref]

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

R. Calderbank and S. Jafarpour, “Finding needles in compressed haystacks,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2012), pp. 3441–3444.

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Problems 23(3), 969–985 (2007).

[Crossref]

E. J. Candès, “Compressive sampling,” in Proceedings of the international congress of mathematicians, (2006), pp. 1433–1452.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

J. Bacca, C. V. Correa, and H. Arguello, “Noniterative hyperspectral image reconstruction from compressive fused measurements,” IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 12(4), 1231–1239 (2019).

[Crossref]

H. Garcia, C. V. Correa, and H. Arguello, “Multi-resolution compressive spectral imaging reconstruction from single pixel measurements,” IEEE Transactions on Image Process. 27(12), 6174–6184 (2018).

[Crossref]

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, (2013), pp. 1139–1147.

M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Top. Signal Process. 4(2), 445–460 (2010).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. intelligence neuroscience 2018, 1–13 (2018).

[Crossref]

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. intelligence neuroscience 2018, 1–13 (2018).

[Crossref]

S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation 4(1), 1–58 (1992).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).

[Crossref]

E. Zisselman, A. Adler, and M. Elad, “Compressed learning for image classification: A deep neural network approach,” Process. Anal. Learn. Images, Shapes, Forms 19, 3–17 (2018).

[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

L. Galvis, D. Lau, X. Ma, H. Arguello, and G. R. Arce, “Coded aperture design in compressive spectral imaging based on side information,” Appl. Opt. 56(22), 6332–6340 (2017).

[Crossref]

L. Galvis, H. Arguello, and G. R. Arce, “Coded aperture design in mismatched compressive spectral imaging,” Appl. Opt. 54(33), 9875–9882 (2015).

[Crossref]

H. Garcia, C. V. Correa, and H. Arguello, “Multi-resolution compressive spectral imaging reconstruction from single pixel measurements,” IEEE Transactions on Image Process. 27(12), 6174–6184 (2018).

[Crossref]

S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation 4(1), 1–58 (1992).

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).

[Crossref]

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).

[Crossref]

C. Hinojosa, J. Bacca, and H. Arguello, “Coded aperture design for compressive spectral subspace clustering,” IEEE J. Sel. Top. Signal Process. 12(6), 1589–1600 (2018).

[Crossref]

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, (2013), pp. 1139–1147.

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Tech. rep., Citeseer (2009).

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.

R. Calderbank and S. Jafarpour, “Finding needles in compressed haystacks,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2012), pp. 3441–3444.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, (2015), pp. 1–15.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

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.

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Tech. rep., Citeseer (2009).

S. Lohit, K. Kulkarni, and P. Turaga, “Direct inference on compressive measurements using convolutional neural networks,” in 2016 IEEE International Conference on Image Processing (ICIP), (IEEE, 2016), pp. 1913–1917.

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).

[Crossref]

V. N. Xuan and O. Loffeld, “A deep learning framework for compressed learning and signal reconstruction,” in 5th International Workshop on Compressed Sensing applied to Radar, Multimodal Sensing, and Imaging (CoSeRa), (2018), pp. 1–5.

S. Lohit, K. Kulkarni, and P. Turaga, “Direct inference on compressive measurements using convolutional neural networks,” in 2016 IEEE International Conference on Image Processing (ICIP), (IEEE, 2016), pp. 1913–1917.

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, (2013), pp. 1139–1147.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

J. Bacca, H. Vargas-García, D. Molina-Velasco, and H. Arguello, “Single pixel compressive spectral polarization imaging using a movable micro-polarizer array,” Revista Fac. de Ing. Universidad de Antioquia pp. 91–99 (2018).

A. Mousavi and R. G. Baraniuk, “Learning to invert: Signal recovery via deep convolutional networks,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2017), pp. 2272–2276.

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).

[Crossref]

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).

[Crossref]

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. intelligence neuroscience 2018, 1–13 (2018).

[Crossref]

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Problems 23(3), 969–985 (2007).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, (2013), pp. 1139–1147.

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.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

S. Lohit, K. Kulkarni, and P. Turaga, “Direct inference on compressive measurements using convolutional neural networks,” in 2016 IEEE International Conference on Image Processing (ICIP), (IEEE, 2016), pp. 1913–1917.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

J. Bacca, H. Vargas-García, D. Molina-Velasco, and H. Arguello, “Single pixel compressive spectral polarization imaging using a movable micro-polarizer array,” Revista Fac. de Ing. Universidad de Antioquia pp. 91–99 (2018).

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. intelligence neuroscience 2018, 1–13 (2018).

[Crossref]

M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Top. Signal Process. 4(2), 445–460 (2010).

[Crossref]

V. N. Xuan and O. Loffeld, “A deep learning framework for compressed learning and signal reconstruction,” in 5th International Workshop on Compressed Sensing applied to Radar, Multimodal Sensing, and Imaging (CoSeRa), (2018), pp. 1–5.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

E. Zisselman, A. Adler, and M. Elad, “Compressed learning for image classification: A deep neural network approach,” Process. Anal. Learn. Images, Shapes, Forms 19, 3–17 (2018).

[Crossref]

H. Arguello, H. Rueda, Y. Wu, D. W. Prather, and G. R. Arce, “Higher-order computational model for coded aperture spectral imaging,” Appl. Opt. 52(10), D12–D21 (2013).

[Crossref]

L. Galvis, H. Arguello, and G. R. Arce, “Coded aperture design in mismatched compressive spectral imaging,” Appl. Opt. 54(33), 9875–9882 (2015).

[Crossref]

L. Galvis, D. Lau, X. Ma, H. Arguello, and G. R. Arce, “Coded aperture design in compressive spectral imaging based on side information,” Appl. Opt. 56(22), 6332–6340 (2017).

[Crossref]

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. intelligence neuroscience 2018, 1–13 (2018).

[Crossref]

J. Bacca, C. V. Correa, and H. Arguello, “Noniterative hyperspectral image reconstruction from compressive fused measurements,” IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 12(4), 1231–1239 (2019).

[Crossref]

M. A. Davenport, P. T. Boufounos, M. B. Wakin, and R. G. Baraniuk, “Signal processing with compressive measurements,” IEEE J. Sel. Top. Signal Process. 4(2), 445–460 (2010).

[Crossref]

C. Hinojosa, J. Bacca, and H. Arguello, “Coded aperture design for compressive spectral subspace clustering,” IEEE J. Sel. Top. Signal Process. 12(6), 1589–1600 (2018).

[Crossref]

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).

[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. E. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).

[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

H. Garcia, C. V. Correa, and H. Arguello, “Multi-resolution compressive spectral imaging reconstruction from single pixel measurements,” IEEE Transactions on Image Process. 27(12), 6174–6184 (2018).

[Crossref]

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Problems 23(3), 969–985 (2007).

[Crossref]

S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation 4(1), 1–58 (1992).

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).

[Crossref]

E. Zisselman, A. Adler, and M. Elad, “Compressed learning for image classification: A deep neural network approach,” Process. Anal. Learn. Images, Shapes, Forms 19, 3–17 (2018).

[Crossref]

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).

[Crossref]

A. Mousavi and R. G. Baraniuk, “Learning to invert: Signal recovery via deep convolutional networks,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2017), pp. 2272–2276.

K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed measurements,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), pp. 449–458.

E. J. Candès, “Compressive sampling,” in Proceedings of the international congress of mathematicians, (2006), pp. 1433–1452.

L. Bottou, “Large-scale machine learning with stochastic gradient descent,” in Proceedings of COMPSTAT’2010, (Springer, 2010), pp. 177–186.

I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the importance of initialization and momentum in deep learning,” in International conference on machine learning, (2013), pp. 1139–1147.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, (2015), pp. 1–15.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 pp. 1–15 (2016).

V. N. Xuan and O. Loffeld, “A deep learning framework for compressed learning and signal reconstruction,” in 5th International Workshop on Compressed Sensing applied to Radar, Multimodal Sensing, and Imaging (CoSeRa), (2018), pp. 1–5.

S. Lohit, K. Kulkarni, and P. Turaga, “Direct inference on compressive measurements using convolutional neural networks,” in 2016 IEEE International Conference on Image Processing (ICIP), (IEEE, 2016), pp. 1913–1917.

R. Calderbank and S. Jafarpour, “Finding needles in compressed haystacks,” in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2012), pp. 3441–3444.

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Tech. rep., Citeseer (2009).

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.

J. Bacca, H. Vargas-García, D. Molina-Velasco, and H. Arguello, “Single pixel compressive spectral polarization imaging using a movable micro-polarizer array,” Revista Fac. de Ing. Universidad de Antioquia pp. 91–99 (2018).