B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010).

[CrossRef]

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

[CrossRef]

R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24(4), 118–121 (2007).

[CrossRef]

V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).

A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: recovering low-rank and sparse matrices from compressive measurements,” in Advances in Neural Information Processing Systems (2011), pp. 1089–1097.

A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in IEEE International Conference on Computational Photography (ICCP) (2012), pp. 1–10.

M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: uncertainty relations and efficient recovery,” IEEE Trans. Signal Process. 58, 3042–3054 (2010).

[CrossRef]

D. Bottisti and R. Muise, “Tree-based adaptive measurement design for compressive imaging under device constraints,” Proc. SPIE 8748, 874802 (2013).

[CrossRef]

M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).

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

[CrossRef]

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010).

[CrossRef]

V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).

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

[CrossRef]

M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).

M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008).

[CrossRef]

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010).

[CrossRef]

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

[CrossRef]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: uncertainty relations and efficient recovery,” IEEE Trans. Signal Process. 58, 3042–3054 (2010).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[CrossRef]

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010).

[CrossRef]

V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).

J. Huang and T. Zhang, “The benefit of group sparsity,” Annals Stat. 38, 1978–2004 (2010).

V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).

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

[CrossRef]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: uncertainty relations and efficient recovery,” IEEE Trans. Signal Process. 58, 3042–3054 (2010).

[CrossRef]

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

[CrossRef]

M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

J. Liang and T. D. Tran, “Fast multiplierless approximations of the DCT with the lifting scheme,” IEEE Trans. Signal Process. 49, 3032–3044 (2001).

[CrossRef]

M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008).

[CrossRef]

D. Bottisti and R. Muise, “Tree-based adaptive measurement design for compressive imaging under device constraints,” Proc. SPIE 8748, 874802 (2013).

[CrossRef]

C. Tsai and D. G. Nishimura, “Reduced aliasing artifacts using variable-density k-space sampling trajectories,” Magn. Reson. Med. 43, 452–458 (2000).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[CrossRef]

M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008).

[CrossRef]

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).

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

[CrossRef]

A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in IEEE International Conference on Computational Photography (ICCP) (2012), pp. 1–10.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: recovering low-rank and sparse matrices from compressive measurements,” in Advances in Neural Information Processing Systems (2011), pp. 1089–1097.

M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008).

[CrossRef]

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in IEEE International Conference on Computational Photography (ICCP) (2012), pp. 1–10.

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

[CrossRef]

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

[CrossRef]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

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

[CrossRef]

J. Liang and T. D. Tran, “Fast multiplierless approximations of the DCT with the lifting scheme,” IEEE Trans. Signal Process. 49, 3032–3044 (2001).

[CrossRef]

C. Tsai and D. G. Nishimura, “Reduced aliasing artifacts using variable-density k-space sampling trajectories,” Magn. Reson. Med. 43, 452–458 (2000).

[CrossRef]

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: recovering low-rank and sparse matrices from compressive measurements,” in Advances in Neural Information Processing Systems (2011), pp. 1089–1097.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

J. Huang and T. Zhang, “The benefit of group sparsity,” Annals Stat. 38, 1978–2004 (2010).

J. Huang and T. Zhang, “The benefit of group sparsity,” Annals Stat. 38, 1978–2004 (2010).

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

[CrossRef]

R. G. Baraniuk, “Compressive sensing [lecture notes],” IEEE Signal Process. Mag. 24(4), 118–121 (2007).

[CrossRef]

M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Process. Mag. 25(2), 72–82 (2008).

[CrossRef]

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

[CrossRef]

R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, “Model-based compressive sensing,” IEEE Trans. Inf. Theory 56, 1982–2001 (2010).

[CrossRef]

Y. C. Eldar, P. Kuppinger, and H. Bolcskei, “Block-sparse signals: uncertainty relations and efficient recovery,” IEEE Trans. Signal Process. 58, 3042–3054 (2010).

[CrossRef]

J. Liang and T. D. Tran, “Fast multiplierless approximations of the DCT with the lifting scheme,” IEEE Trans. Signal Process. 49, 3032–3044 (2001).

[CrossRef]

C. Tsai and D. G. Nishimura, “Reduced aliasing artifacts using variable-density k-space sampling trajectories,” Magn. Reson. Med. 43, 452–458 (2000).

[CrossRef]

D. Bottisti and R. Muise, “Tree-based adaptive measurement design for compressive imaging under device constraints,” Proc. SPIE 8748, 874802 (2013).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

[CrossRef]

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, “Breaking the coherence barrier: asymptotic incoherence and asymptotic sparsity in compressed sensing,” arXiv:1302.0561 (2013).

E. Van Den Berg and M. P. Friedlander, “SPGL1: a solver for large-scale sparse reconstruction,” http://www.cs.ubc.ca/labs/scl/spgl1 , 2007.

V. Cevher, P. Indyk, C. Hegde, and R. G. Baraniuk, “Recovery of clustered sparse signals from compressive measurements,” (2009).

M. A. Davenport, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, “A simple proof that random matrices are democratic,” arXiv:0911.0736 (2009).

D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of 45rd Annual Allerton Conference on Communication, Control, and Computing, Allerton, Illinois,2005.

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in Electronic Imaging 2006 (International Society for Optics and Photonics, 2006), p. 606509.

A. C. Sankaranarayanan, C. Studer, and R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in IEEE International Conference on Computational Photography (ICCP) (2012), pp. 1–10.

L. Xu, A. C. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, and K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2C.4.

T. Goldstein, L. Xu, K. F. Kelly, and R. G. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.34056 (2013).

A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, “SpaRCS: recovering low-rank and sparse matrices from compressive measurements,” in Advances in Neural Information Processing Systems (2011), pp. 1089–1097.

“Columbus large image format (CLIF) 2007 dataset,” https://www.sdms.afrl.af.mil/index.php?collection=clif2007 .