J. V. Thompson, J. N. Bixler, B. H. Hokr, G. D. Noojin, M. O. Scully, and V. V. Yakovlev, “Single-shot chemical detection and identification with compressed hyperspectral raman imaging,” Opt. Lett. 42, 2169–2172 (2017).

[Crossref]
[PubMed]

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

Y. Li, F. Li, B. Bai, and Q. Shen, “Image fusion via nonlocal sparse k-svd dictionary learning,” Appl. Opt. 55, 1814–1823 (2016).

[Crossref]
[PubMed]

P. Meza, E. Vera, and J. Martinez, “Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization,” Proc. IS&T international Symposium on Electronic Imaging 2016, 1–5 (2016).

P. Meza, J. E. Pezoa, and S. N. Torres, “Multidimensional striping noise compensation in hyperspectral imaging: Exploiting hypercubes’s spatial, spectral, and temporal redundancy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 4428–4441 (2016).

[Crossref]

M. E. Gehm and D. J. Brady, “Compressive sensing in the eo/ir,” Appl. Opt. 54, C14–C22 (2015).

[Crossref]
[PubMed]

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

D. S. Antony and G. Rathna, “Gpu based fast non local means algorithm,” Journal of Image and Graphics 3, 122 (2015).

S. Cuomo, P. D. Michele, and F. Piccialli, “3d data denoising via nonlocal means filter by using parallel gpu strategies,” Computational and mathematical methods in medicine 2014, 523862 (2014).

[Crossref]
[PubMed]

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Transactions on Signal Processing 59, 4053–4085 (2011).

[Crossref]

S. Becker, J. Bobin, and E. J. Candés, “Nesta: A fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences 4, 1–39 (2011).

[Crossref]

R. Ward, “Compressed sensing with cross validation,” IEEE Transactions on Information Theory 55, 5773–5782 (2009).

[Crossref]

D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis 26, 301–321 (2009).

[Crossref]

T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM Journal on Imaging Sciences 2, 323–343 (2009).

[Crossref]

A. Wagadarikar, R. John, R. Willett, and D. Brady, “Single disperser design for coded aperture snapshot spectral imaging,” Appl. Opt. 47, B44–B51 (2008).

[Crossref]
[PubMed]

L. Gómez-Chova, L. Alonso, L. Guanter, G. Camps-Valls, J. Calpe, and J. Moreno, “Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images,” Appl. Opt. 47, F46–F60 (2008).

[Crossref]
[PubMed]

J. Bioucas-Dias and M. Figueiredo, “A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Transactions on Image Processing 16, 2992–3004 (2007).

[Crossref]
[PubMed]

E. Candes and T. Tao, “The dantzig selector: Statistical estimation when p is much larger than n,” The Annals of Statistics 35, 2313–2351 (2007).

[Crossref]

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).

[Crossref]
[PubMed]

J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory 53, 4655–4666 (2007).

[Crossref]

D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory 52, 1289–1306 (2006).

[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory 52, 489–509 (2006).

[Crossref]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition 2, 60–65 (2005).

H. Tian, B. Fowler, and A. Gamal, “Analysis of temporal noise in cmos photodiode active pixel sensor,” IEEE Journal of Solid-State Circuits 36, 92–101 (2001).

[Crossref]

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing 20, 33–61 (1998).

[Crossref]

D. S. Antony and G. Rathna, “Gpu based fast non local means algorithm,” Journal of Image and Graphics 3, 122 (2015).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

S. Becker, J. Bobin, and E. J. Candés, “Nesta: A fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences 4, 1–39 (2011).

[Crossref]

J. Bioucas-Dias and M. Figueiredo, “A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Transactions on Image Processing 16, 2992–3004 (2007).

[Crossref]
[PubMed]

S. Becker, J. Bobin, and E. J. Candés, “Nesta: A fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences 4, 1–39 (2011).

[Crossref]

M. Borengasser, W. S. Hungate, and R. L. Watkins, Hyperspectral Remote Sensing: Principles and Applications (CRC, 2008).

M. E. Gehm and D. J. Brady, “Compressive sensing in the eo/ir,” Appl. Opt. 54, C14–C22 (2015).

[Crossref]
[PubMed]

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).

[Crossref]
[PubMed]

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition 2, 60–65 (2005).

E. Candes and T. Tao, “The dantzig selector: Statistical estimation when p is much larger than n,” The Annals of Statistics 35, 2313–2351 (2007).

[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory 52, 489–509 (2006).

[Crossref]

S. Becker, J. Bobin, and E. J. Candés, “Nesta: A fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences 4, 1–39 (2011).

[Crossref]

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing 20, 33–61 (1998).

[Crossref]

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition 2, 60–65 (2005).

S. Cuomo, P. D. Michele, and F. Piccialli, “3d data denoising via nonlocal means filter by using parallel gpu strategies,” Computational and mathematical methods in medicine 2014, 523862 (2014).

[Crossref]
[PubMed]

D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory 52, 1289–1306 (2006).

[Crossref]

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing 20, 33–61 (1998).

[Crossref]

M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Transactions on Signal Processing 59, 4053–4085 (2011).

[Crossref]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Transactions on Signal Processing 59, 4053–4085 (2011).

[Crossref]

J. Bioucas-Dias and M. Figueiredo, “A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Transactions on Image Processing 16, 2992–3004 (2007).

[Crossref]
[PubMed]

H. Tian, B. Fowler, and A. Gamal, “Analysis of temporal noise in cmos photodiode active pixel sensor,” IEEE Journal of Solid-State Circuits 36, 92–101 (2001).

[Crossref]

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

H. Tian, B. Fowler, and A. Gamal, “Analysis of temporal noise in cmos photodiode active pixel sensor,” IEEE Journal of Solid-State Circuits 36, 92–101 (2001).

[Crossref]

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

M. E. Gehm and D. J. Brady, “Compressive sensing in the eo/ir,” Appl. Opt. 54, C14–C22 (2015).

[Crossref]
[PubMed]

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).

[Crossref]
[PubMed]

J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory 53, 4655–4666 (2007).

[Crossref]

T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM Journal on Imaging Sciences 2, 323–343 (2009).

[Crossref]

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

M. Borengasser, W. S. Hungate, and R. L. Watkins, Hyperspectral Remote Sensing: Principles and Applications (CRC, 2008).

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

A. Wagadarikar, R. John, R. Willett, and D. Brady, “Single disperser design for coded aperture snapshot spectral imaging,” Appl. Opt. 47, B44–B51 (2008).

[Crossref]
[PubMed]

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).

[Crossref]
[PubMed]

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest p, CTuA5 (2009).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

P. Meza, E. Vera, and J. Martinez, “Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization,” Proc. IS&T international Symposium on Electronic Imaging 2016, 1–5 (2016).

P. Meza, E. Vera, and J. Martinez, “Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization,” Proc. IS&T international Symposium on Electronic Imaging 2016, 1–5 (2016).

P. Meza, J. E. Pezoa, and S. N. Torres, “Multidimensional striping noise compensation in hyperspectral imaging: Exploiting hypercubes’s spatial, spectral, and temporal redundancy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 4428–4441 (2016).

[Crossref]

S. Cuomo, P. D. Michele, and F. Piccialli, “3d data denoising via nonlocal means filter by using parallel gpu strategies,” Computational and mathematical methods in medicine 2014, 523862 (2014).

[Crossref]
[PubMed]

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition 2, 60–65 (2005).

D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis 26, 301–321 (2009).

[Crossref]

T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM Journal on Imaging Sciences 2, 323–343 (2009).

[Crossref]

P. Meza, J. E. Pezoa, and S. N. Torres, “Multidimensional striping noise compensation in hyperspectral imaging: Exploiting hypercubes’s spatial, spectral, and temporal redundancy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 4428–4441 (2016).

[Crossref]

S. Cuomo, P. D. Michele, and F. Piccialli, “3d data denoising via nonlocal means filter by using parallel gpu strategies,” Computational and mathematical methods in medicine 2014, 523862 (2014).

[Crossref]
[PubMed]

D. S. Antony and G. Rathna, “Gpu based fast non local means algorithm,” Journal of Image and Graphics 3, 122 (2015).

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory 52, 489–509 (2006).

[Crossref]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing 20, 33–61 (1998).

[Crossref]

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

Y. Li, F. Li, B. Bai, and Q. Shen, “Image fusion via nonlocal sparse k-svd dictionary learning,” Appl. Opt. 55, 1814–1823 (2016).

[Crossref]
[PubMed]

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest p, CTuA5 (2009).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

E. Candes and T. Tao, “The dantzig selector: Statistical estimation when p is much larger than n,” The Annals of Statistics 35, 2313–2351 (2007).

[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory 52, 489–509 (2006).

[Crossref]

H. Tian, B. Fowler, and A. Gamal, “Analysis of temporal noise in cmos photodiode active pixel sensor,” IEEE Journal of Solid-State Circuits 36, 92–101 (2001).

[Crossref]

P. Meza, J. E. Pezoa, and S. N. Torres, “Multidimensional striping noise compensation in hyperspectral imaging: Exploiting hypercubes’s spatial, spectral, and temporal redundancy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 4428–4441 (2016).

[Crossref]

D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis 26, 301–321 (2009).

[Crossref]

J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory 53, 4655–4666 (2007).

[Crossref]

P. Meza, E. Vera, and J. Martinez, “Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization,” Proc. IS&T international Symposium on Electronic Imaging 2016, 1–5 (2016).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

R. Ward, “Compressed sensing with cross validation,” IEEE Transactions on Information Theory 55, 5773–5782 (2009).

[Crossref]

M. Borengasser, W. S. Hungate, and R. L. Watkins, Hyperspectral Remote Sensing: Principles and Applications (CRC, 2008).

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

A. Wagadarikar, R. John, R. Willett, and D. Brady, “Single disperser design for coded aperture snapshot spectral imaging,” Appl. Opt. 47, B44–B51 (2008).

[Crossref]
[PubMed]

M. E. Gehm and D. J. Brady, “Compressive sensing in the eo/ir,” Appl. Opt. 54, C14–C22 (2015).

[Crossref]
[PubMed]

L. Gómez-Chova, L. Alonso, L. Guanter, G. Camps-Valls, J. Calpe, and J. Moreno, “Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images,” Appl. Opt. 47, F46–F60 (2008).

[Crossref]
[PubMed]

Y. Li, F. Li, B. Bai, and Q. Shen, “Image fusion via nonlocal sparse k-svd dictionary learning,” Appl. Opt. 55, 1814–1823 (2016).

[Crossref]
[PubMed]

D. Needell and J. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Applied and Computational Harmonic Analysis 26, 301–321 (2009).

[Crossref]

S. Cuomo, P. D. Michele, and F. Piccialli, “3d data denoising via nonlocal means filter by using parallel gpu strategies,” Computational and mathematical methods in medicine 2014, 523862 (2014).

[Crossref]
[PubMed]

C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications 56, 507–530 (2013).

[Crossref]

J. Zhang, S. Liu, R. Xiong, S. Ma, and D. Zhao, “Improved total variation based image compressive sensing recovery by nonlocal regularization,” IEEE International Symposium on Circuits and Systems, 2013, 2836–2839 (2013).

P. Meza, J. E. Pezoa, and S. N. Torres, “Multidimensional striping noise compensation in hyperspectral imaging: Exploiting hypercubes’s spatial, spectral, and temporal redundancy,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 4428–4441 (2016).

[Crossref]

H. Tian, B. Fowler, and A. Gamal, “Analysis of temporal noise in cmos photodiode active pixel sensor,” IEEE Journal of Solid-State Circuits 36, 92–101 (2001).

[Crossref]

J. Bioucas-Dias and M. Figueiredo, “A new twist: Two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Transactions on Image Processing 16, 2992–3004 (2007).

[Crossref]
[PubMed]

R. Ward, “Compressed sensing with cross validation,” IEEE Transactions on Information Theory 55, 5773–5782 (2009).

[Crossref]

D. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory 52, 1289–1306 (2006).

[Crossref]

E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory 52, 489–509 (2006).

[Crossref]

J. Tropp and A. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory 53, 4655–4666 (2007).

[Crossref]

M. F. Duarte and Y. C. Eldar, “Structured compressed sensing: From theory to applications,” IEEE Transactions on Signal Processing 59, 4053–4085 (2011).

[Crossref]

D. S. Antony and G. Rathna, “Gpu based fast non local means algorithm,” Journal of Image and Graphics 3, 122 (2015).

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516, 74–77 (2014).

[Crossref]
[PubMed]

A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” Proc. IEEE Conference on Computer Vision and Pattern Recognition 2, 60–65 (2005).

P. Meza, E. Vera, and J. Martinez, “Improved reconstruction for compressive hyperspectral imaging using spatial-spectral non-local means regularization,” Proc. IS&T international Symposium on Electronic Imaging 2016, 1–5 (2016).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” Proc. SPIE 6065, 606501 (2006).

A. E. Gamal, B. A. Fowler, H. Min, and X. Liu, “Modeling and estimation of fpn components in cmos image sensors,” Proc. SPIE 3301, 330101 (1998).

J. Yang, Y.J. Li, C.-W. Chan, and Q. Shen, “Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation,” Remote Sensing 9, 53 (2017).

W. Huang, L. Xiao, H. Liu, and Z. Wei, “Hyperspectral imagery super-resolution by compressive sensing inspired dictionary learning and spatial-spectral regularization,” Sensors 15, 2041–2058 (2015).

[Crossref]
[PubMed]

T. Goldstein and S. Osher, “The split bregman method for l1-regularized problems,” SIAM Journal on Imaging Sciences 2, 323–343 (2009).

[Crossref]

S. Becker, J. Bobin, and E. J. Candés, “Nesta: A fast and accurate first-order method for sparse recovery,” SIAM Journal on Imaging Sciences 4, 1–39 (2011).

[Crossref]

S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal on Scientific Computing 20, 33–61 (1998).

[Crossref]

E. Candes and T. Tao, “The dantzig selector: Statistical estimation when p is much larger than n,” The Annals of Statistics 35, 2313–2351 (2007).

[Crossref]

M. Borengasser, W. S. Hungate, and R. L. Watkins, Hyperspectral Remote Sensing: Principles and Applications (CRC, 2008).

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest p, CTuA5 (2009).