Abstract

Several hyperspectral (HS) systems based on compressive sensing (CS) theory have been presented to capture HS images with high accuracy and with a lower number of measurements than needed by conventional systems. However, the reconstruction of HS compressed measurements is time-consuming and commonly involves hyperparameter tuning per each scenario. In this paper, we introduce a Convolutional Neural Network (CNN) designed for the reconstruction of HS cubes captured with CS imagers based on spectral modulation. Our Deep Neural Network (DNN), dubbed DeepCubeNet, provides significant reduction in the reconstruction time compared to classical iterative methods. The performance of DeepCubeNet is investigated on simulated data, and we demonstrate for the first time, to the best of our knowledge, real reconstruction of CS HS measurements using DNN. We demonstrate significantly enhanced reconstruction accuracy compared to iterative CS reconstruction, as well as improvement in reconstruction time by many orders of magnitude.

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

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References

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    [Crossref]

2019 (3)

C. Kim, D. Park, and H. Lee, “Convolutional neural networks for the reconstruction of spectra in compressive sensing spectrometers,” Proc. SPIE 10937, 109370L (2019).
[Crossref]

L. Wang, T. Zhang, Y. Fu, and H. Huang, “HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,” IEEE Trans. Image Process. 28(5), 2257–2270 (2019).
[Crossref]

Y. Heiser, Y. Oiknine, and A. Stern, “Compressive hyperspectral image reconstruction with deep neural networks,” Proc. SPIE 10989, 109890M (2019).
[Crossref]

2018 (7)

X. Wang, Y. Zhang, X. Ma, T. Xu, and G. R. Arce, “Compressive spectral imaging system based on liquid crystal tunable filter,” Opt. Express 26(19), 25226–25243 (2018).
[Crossref]

Y. Oiknine, I. August, D. G. Blumberg, and A. Stern, “NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator,” J. Opt. 20(4), 044011 (2018).
[Crossref]

Y. Oiknine, I. August, and A. Stern, “Multi-aperture snapshot compressive hyperspectral camera,” Opt. Lett. 43(20), 5042–5045 (2018).
[Crossref]

Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
[Crossref]

A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using deep neural networks for inverse problems in imaging: beyond analytical methods,” IEEE Signal Process. Mag. 35(1), 20–36 (2018).
[Crossref]

Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Günaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery,” Optica 5(6), 704–710 (2018).
[Crossref]

T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah, “Deep learning approach for Fourier ptychography microscopy,” Opt. Express 26(20), 26470–26484 (2018).
[Crossref]

2017 (7)

G. Satat, M. Tancik, O. Gupta, B. Heshmat, and R. Raskar, “Object classification through scattering media with deep learning on time resolved measurement,” Opt. Express 25(15), 17466–17479 (2017).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
[Crossref]

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
[Crossref]

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

S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, “Hyperspectral image spatial super-resolution via 3D full convolutional neural network,” Remote Sens. 9(11), 1139 (2017).
[Crossref]

A. Sinha, J. Lee, S. Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4(9), 1117–1125 (2017).
[Crossref]

2016 (3)

M. A. Golub, A. Averbuch, M. Nathan, V. A. Zheludev, J. Hauser, S. Gurevitch, R. Malinsky, and A. Kagan, “Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser,” Appl. Opt. 55(3), 432–443 (2016).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultra-spectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6(1), 23524 (2016).
[Crossref]

2015 (2)

2014 (2)

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]

X. Lin, G. Wetzstein, Y. Liu, and Q. Dai, “Dual-coded compressive hyperspectral imaging,” Opt. Lett. 39(7), 2044–2047 (2014).
[Crossref]

2013 (1)

2009 (1)

Y. Rivenson and A. Stern, “Compressed imaging with a separable sensing operator,” IEEE Signal Process. Lett. 16(6), 449–452 (2009).
[Crossref]

2007 (1)

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref]

Abdulhalim, I.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultra-spectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6(1), 23524 (2016).
[Crossref]

AbuLeil, M.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultra-spectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6(1), 23524 (2016).
[Crossref]

Arad, B.

B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural RGB images,” In European Conference on Computer Vision, pp. 19–34 (Springer, 2016).

Arce, G. R.

X. Wang, Y. Zhang, X. Ma, T. Xu, and G. R. Arce, “Compressive spectral imaging system based on liquid crystal tunable filter,” Opt. Express 26(19), 25226–25243 (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]

G. R. Arce, H. Rueda, C. V. Correa, A. Ramirez, and H. Arguello, “Snapshot compressive multispectral cameras,” In Wiley Encyclopedia of Electrical and Electronics Engineering; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–22.

Arguello, H.

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]

G. R. Arce, H. Rueda, C. V. Correa, A. Ramirez, and H. Arguello, “Snapshot compressive multispectral cameras,” In Wiley Encyclopedia of Electrical and Electronics Engineering; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–22.

Ashok, A.

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

August, I.

Y. Oiknine, I. August, and A. Stern, “Multi-aperture snapshot compressive hyperspectral camera,” Opt. Lett. 43(20), 5042–5045 (2018).
[Crossref]

Y. Oiknine, I. August, D. G. Blumberg, and A. Stern, “NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator,” J. Opt. 20(4), 044011 (2018).
[Crossref]

Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
[Crossref]

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
[Crossref]

D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
[Crossref]

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultra-spectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6(1), 23524 (2016).
[Crossref]

August, Y.

Averbuch, A.

Barbastathis, G.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Ben-Shahar, O.

B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural RGB images,” In European Conference on Computer Vision, pp. 19–34 (Springer, 2016).

Bioucas-Dias, J. M.

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref]

Blumberg, D. G.

Y. Oiknine, I. August, D. G. Blumberg, and A. Stern, “NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator,” J. Opt. 20(4), 044011 (2018).
[Crossref]

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
[Crossref]

D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
[Crossref]

Brady, D. J.

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]

Brox, T.

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, pp. 234–241 (Springer, 2015).

Carin, L.

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]

Chakrabarti, A.

A. Chakrabarti and T. Zickler, “Statistics of real-world hyperspectral images,” In CVPR 2011, pp. 193–200 (IEEE, 2011).

Chen, C.

Z. Shi, C. Chen, Z. Xiong, D. Liu, and F. Wu, “Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 939–947 (2018).

Chen, E.

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” In Advances in neural information processing systems, pp. 341–349 (2012).

Correa, C. V.

G. R. Arce, H. Rueda, C. V. Correa, A. Ramirez, and H. Arguello, “Snapshot compressive multispectral cameras,” In Wiley Encyclopedia of Electrical and Electronics Engineering; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–22.

Dai, Q.

Dong, C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

Du, Q.

S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, “Hyperspectral image spatial super-resolution via 3D full convolutional neural network,” Remote Sens. 9(11), 1139 (2017).
[Crossref]

Farber, V.

Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
[Crossref]

Figueiredo, M. A.

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref]

Fischer, P.

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, pp. 234–241 (Springer, 2015).

Froustey, E.

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

Fu, Y.

L. Wang, T. Zhang, Y. Fu, and H. Huang, “HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,” IEEE Trans. Image Process. 28(5), 2257–2270 (2019).
[Crossref]

Gedalin, D.

Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
[Crossref]

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
[Crossref]

D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
[Crossref]

Gedaln, D.

D. Gedaln, “DeepCubeNetPublic,” https://github.com/dngedalin/DeepCubeNetPublic .

Golub, M. A.

Göröcs, Z.

Goy, A.

Günaydin, H.

Gupta, O.

Gurevitch, S.

Hauser, J.

He, K.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

Heiser, Y.

Y. Heiser, Y. Oiknine, and A. Stern, “Compressive hyperspectral image reconstruction with deep neural networks,” Proc. SPIE 10989, 109890M (2019).
[Crossref]

Heshmat, B.

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Huang, H.

L. Wang, T. Zhang, Y. Fu, and H. Huang, “HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,” IEEE Trans. Image Process. 28(5), 2257–2270 (2019).
[Crossref]

Iliadis, M.

A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using deep neural networks for inverse problems in imaging: beyond analytical methods,” IEEE Signal Process. Mag. 35(1), 20–36 (2018).
[Crossref]

Ji, J.

S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, “Hyperspectral image spatial super-resolution via 3D full convolutional neural network,” Remote Sens. 9(11), 1139 (2017).
[Crossref]

Jin, K. H.

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

Kagan, A.

Kamilov, U. S.

Katsaggelos, A. K.

A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using deep neural networks for inverse problems in imaging: beyond analytical methods,” IEEE Signal Process. Mag. 35(1), 20–36 (2018).
[Crossref]

Kerviche, R.

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

Kim, C.

C. Kim, D. Park, and H. Lee, “Convolutional neural networks for the reconstruction of spectra in compressive sensing spectrometers,” Proc. SPIE 10937, 109370L (2019).
[Crossref]

Kim, J.

J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1646–1654 (2016).

Kittle, D. S.

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]

Kulkarni, K.

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

Kwon Lee, J.

J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1646–1654 (2016).

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Lee, H.

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Liu, Y.

Lohit, S.

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

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

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Y. Heiser, Y. Oiknine, and A. Stern, “Compressive hyperspectral image reconstruction with deep neural networks,” Proc. SPIE 10989, 109890M (2019).
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Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
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[Crossref]

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C. Kim, D. Park, and H. Lee, “Convolutional neural networks for the reconstruction of spectra in compressive sensing spectrometers,” Proc. SPIE 10937, 109370L (2019).
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Ramirez, A.

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Rivenson, Y.

Ronneberger, O.

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, pp. 234–241 (Springer, 2015).

Rotman, S. R.

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
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D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
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G. R. Arce, H. Rueda, C. V. Correa, A. Ramirez, and H. Arguello, “Snapshot compressive multispectral cameras,” In Wiley Encyclopedia of Electrical and Electronics Engineering; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–22.

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Z. Shi, C. Chen, Z. Xiong, D. Liu, and F. Wu, “Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 939–947 (2018).

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Y. Heiser, Y. Oiknine, and A. Stern, “Compressive hyperspectral image reconstruction with deep neural networks,” Proc. SPIE 10989, 109890M (2019).
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Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
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Y. Oiknine, I. August, and A. Stern, “Multi-aperture snapshot compressive hyperspectral camera,” Opt. Lett. 43(20), 5042–5045 (2018).
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Y. Oiknine, I. August, D. G. Blumberg, and A. Stern, “NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator,” J. Opt. 20(4), 044011 (2018).
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Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
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D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
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Tian, L.

Turaga, P.

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

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Wang, L.

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J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” In Advances in neural information processing systems, pp. 341–349 (2012).

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Z. Shi, C. Chen, Z. Xiong, D. Liu, and F. Wu, “Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 939–947 (2018).

Xu, L.

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” In Advances in neural information processing systems, pp. 341–349 (2012).

Xu, T.

Xue, Y.

Yariv, A.

A. Yariv and P. Yeh, Optical waves in crystals (Wiley, 1984).

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A. Yariv and P. Yeh, Optical waves in crystals (Wiley, 1984).

Yuan, X.

S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, “Hyperspectral image spatial super-resolution via 3D full convolutional neural network,” Remote Sens. 9(11), 1139 (2017).
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Zhang, T.

L. Wang, T. Zhang, Y. Fu, and H. Huang, “HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,” IEEE Trans. Image Process. 28(5), 2257–2270 (2019).
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Appl. Opt. (2)

IEEE Signal Process. Lett. (1)

Y. Rivenson and A. Stern, “Compressed imaging with a separable sensing operator,” IEEE Signal Process. Lett. 16(6), 449–452 (2009).
[Crossref]

IEEE Signal Process. Mag. (2)

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. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using deep neural networks for inverse problems in imaging: beyond analytical methods,” IEEE Signal Process. Mag. 35(1), 20–36 (2018).
[Crossref]

IEEE Trans. Image Process. (3)

L. Wang, T. Zhang, Y. Fu, and H. Huang, “HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging,” IEEE Trans. Image Process. 28(5), 2257–2270 (2019).
[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
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J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
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IEEE Trans. Pattern Anal. Mach. Intell. (1)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

J. Imaging (1)

Y. Oiknine, I. August, V. Farber, D. Gedalin, and A. Stern, “Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal,” J. Imaging 5(1), 3 (2018).
[Crossref]

J. Opt. (1)

Y. Oiknine, I. August, D. G. Blumberg, and A. Stern, “NIR hyperspectral compressive imager based on a modified Fabry–Perot resonator,” J. Opt. 20(4), 044011 (2018).
[Crossref]

Nature (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref]

Opt. Eng. (1)

D. Gedalin, Y. Oiknine, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system,” Opt. Eng. 56(4), 041312 (2017).
[Crossref]

Opt. Express (3)

Opt. Lett. (2)

Optica (4)

Proc. SPIE (3)

Y. Oiknine, D. Gedalin, I. August, D. G. Blumberg, S. R. Rotman, and A. Stern, “Target detection with compressive sensing hyperspectral images,” Proc. SPIE 10427, 104270O (2017).
[Crossref]

C. Kim, D. Park, and H. Lee, “Convolutional neural networks for the reconstruction of spectra in compressive sensing spectrometers,” Proc. SPIE 10937, 109370L (2019).
[Crossref]

Y. Heiser, Y. Oiknine, and A. Stern, “Compressive hyperspectral image reconstruction with deep neural networks,” Proc. SPIE 10989, 109890M (2019).
[Crossref]

Remote Sens. (1)

S. Mei, X. Yuan, J. Ji, Y. Zhang, S. Wan, and Q. Du, “Hyperspectral image spatial super-resolution via 3D full convolutional neural network,” Remote Sens. 9(11), 1139 (2017).
[Crossref]

Sci. Rep. (1)

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultra-spectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6(1), 23524 (2016).
[Crossref]

Other (11)

G. R. Arce, H. Rueda, C. V. Correa, A. Ramirez, and H. Arguello, “Snapshot compressive multispectral cameras,” In Wiley Encyclopedia of Electrical and Electronics Engineering; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017; pp. 1–22.

A. Stern, Optical compressive imaging (CRC Press, 2016).

J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” In Advances in neural information processing systems, pp. 341–349 (2012).

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

Z. Shi, C. Chen, Z. Xiong, D. Liu, and F. Wu, “Hscnn+: Advanced cnn-based hyperspectral recovery from rgb images,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 939–947 (2018).

J. Kim, J. Kwon Lee, and K. Mu Lee, “Accurate image super-resolution using very deep convolutional networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1646–1654 (2016).

D. Gedaln, “DeepCubeNetPublic,” https://github.com/dngedalin/DeepCubeNetPublic .

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, pp. 234–241 (Springer, 2015).

B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural RGB images,” In European Conference on Computer Vision, pp. 19–34 (Springer, 2016).

A. Yariv and P. Yeh, Optical waves in crystals (Wiley, 1984).

A. Chakrabarti and T. Zickler, “Statistics of real-world hyperspectral images,” In CVPR 2011, pp. 193–200 (IEEE, 2011).

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Figures (10)

Fig. 1.
Fig. 1. CS-MUSI camera.
Fig. 2.
Fig. 2. Visualization of the 32 by 391 CS-MUSI spectral sensing matrix, ${\boldsymbol{\Phi }_\lambda }$, map describing the spectral transmission in the range of 400-800 nm for 32 different voltages applied on the LC.
Fig. 3.
Fig. 3. RGB representations of three of the data set cubes.
Fig. 4.
Fig. 4. Data pair generation: the original HS cube is compressed using the CS matrix, patches are extracted with overlap from both the original HS cube and the CS cube, generating training pairs. ${N_x}$ and ${N_y}$ are the spatial sizes of the cube, ${N_\lambda }$ is the spectral domain size and ${M_\lambda }$ is the number of CS-MUSI measurements.
Fig. 5.
Fig. 5. DeepCubeNet Architecture; a pseudo-inverse operation followed by a three-level U-NET. The size of the output tensor is provided left of the box and then the number of filters is denoted on top of the box. The grey boxes represent copied feature maps.
Fig. 6.
Fig. 6. Initial back-projection using pseudo-inverse operators in the first 2D convolutional operation in Fig. 5.
Fig. 7.
Fig. 7. Spatial resolution preserve: a) Original full resolution ground truth at 500 nm, b) Reconstruction.
Fig. 8.
Fig. 8. (a) - (c) Three examples of spectra for reconstruction with DeepCubeNet (red dashed line), TwIST (dotted solid line), and ground truth (blue solid line).
Fig. 9.
Fig. 9. (a) RGB projection of predicted LEDs HS cube, (b) RGB Image of the RGB LED arrays. (c) Spectra reconstruction of green, blue and red LEDs in Fig. 9(a) compared to a spectrometer ground truths.
Fig. 10.
Fig. 10. (a) RGB image of the three car models. (b) RGB representation of the reconstructed car models HS image. Nine subfigures (c-k) from the entire HS cube are presented corresponding to different wavelengths (482, 490, 518, 536, 566, 588, 595, 630, 642 nm).

Tables (1)

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Table 1. Encoder and decoder architectures

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

ϕ LC ( λ , V i ) = 1 2 1 2 cos ( 2 π Δ n ( V i ) d λ ) ,
g = Φ f .
Φ = [ Φ λ 0 M λ × N λ 0 M λ × N λ 0 M λ × N λ Φ λ 0 M λ × N λ 0 M λ × N λ 0 M λ × N λ Φ λ ] ,
Φ λ = Φ λ T ( Φ λ Φ λ T ) 1 .

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