R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2012).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[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. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[CrossRef]

D. Stowell and M. D. Plumbley, “Fast multidimensional entropy estimation by k-d partitioning,” IEEE Signal Process. Lett. 16, 537–540 (2009).

[CrossRef]

L. He and L. Carin, “Exploiting structure in wavelet-based Bayesian compressive sensing,” IEEE Trans. Signal Process. 57, 3488–3497 (2009).

[CrossRef]

Y. C. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Trans. Inf. Theory 55, 5302–5316 (2009).

[CrossRef]

J. Romberg, “Imaging via compressing sampling,” IEEE Signal Process. Mag. 25, (2) 14–20 (2008).

[CrossRef]

H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory 54, 2210–2219 (2008).

[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging system design using task-specific information,” Appl. Opt. 47, 4457–4471 (2008).

[CrossRef]

M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293–5303 (2007).

[CrossRef]

M. A. Neifeld, A. Ashok, and P. K. Baheti, “Task-specific information for imaging system analysis,” J. Opt. Soc. Am. A 24, B25–B41 (2007).

[CrossRef]

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

[CrossRef]

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Signal Process. 55, 5695–5702 (2007).

[CrossRef]

Y. Tsaig and D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).

[CrossRef]

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).

[CrossRef]

E. Candes and T. Tal, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).

[CrossRef]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5674, 76–86 (2005).

[CrossRef]

E. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51, 4203–4215 (2005).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379–3389 (2003).

[CrossRef]

H. Pal and M. A. Neifeld, “Multispectral principal component imaging,” Opt. Express 11, 2118–2125 (2003).

[CrossRef]

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).

[CrossRef]

D. L. Ruderman, “Origins of scaling in natural images,” Vis. Res. 37, 3385–3398 (1997).

[CrossRef]

D. J. Tolhurst, Y. Tadmor, and T. Chao, “Amplitude spectra of natural images,” Ophthalmic Physiolog. Opt. 12, 229–232 (1992).

[CrossRef]

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 30–44 (1991).

[CrossRef]

W. Chen and W. Pratt, “Scene adaptive coder,” IEEE Trans. Commun. 32, 225–232 (1984).

[CrossRef]

H. Nyquist, “Certain topics in telegraph transmission theory,” Trans. Am. Inst. Electr. Eng. 47, 617–644 (1928).

[CrossRef]

E. T. Whittaker, “On the functions which are represented by the expansions of the interpolation theory,” Proc. R. Soc. Edinburgh 35, 181–194 (1915).

A. Ashok and M. A. Neifeld, “Compressive imaging: hybrid measurement basis design,” J. Opt. Soc. Am. A 28, 1041–1050 (2011).

[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging system design using task-specific information,” Appl. Opt. 47, 4457–4471 (2008).

[CrossRef]

M. A. Neifeld, A. Ashok, and P. K. Baheti, “Task-specific information for imaging system analysis,” J. Opt. Soc. Am. A 24, B25–B41 (2007).

[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging system design using task-specific information,” Appl. Opt. 47, 4457–4471 (2008).

[CrossRef]

M. A. Neifeld, A. Ashok, and P. K. Baheti, “Task-specific information for imaging system analysis,” J. Opt. Soc. Am. A 24, B25–B41 (2007).

[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]

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vol. 19 (MIT, 2007), pp. 801–808.

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

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

[CrossRef]

E. Candes and T. Tal, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).

[CrossRef]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5674, 76–86 (2005).

[CrossRef]

E. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51, 4203–4215 (2005).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

L. He and L. Carin, “Exploiting structure in wavelet-based Bayesian compressive sensing,” IEEE Trans. Signal Process. 57, 3488–3497 (2009).

[CrossRef]

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

S. Ji and L. Carin, “Bayesian compressive sensing and projection optimization,” in Proceedings of the 24th International Conference on Machine Learning (ICML) (ACM, 2007), pp. 377–384.

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

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

[CrossRef]

H. S. Chang, Y. Weiss, and W. T. Freeman, “Informative sensing,” submitted to IEEE Trans. Inf. Theory, preprint available at arXiv http://arxiv.org/abs/0901.4275 (2009).

D. J. Tolhurst, Y. Tadmor, and T. Chao, “Amplitude spectra of natural images,” Ophthalmic Physiolog. Opt. 12, 229–232 (1992).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

W. Chen and W. Pratt, “Scene adaptive coder,” IEEE Trans. Commun. 32, 225–232 (1984).

[CrossRef]

Y. Tsaig and D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).

[CrossRef]

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (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]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Signal Process. 55, 5695–5702 (2007).

[CrossRef]

Y. C. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Trans. Inf. Theory 55, 5302–5316 (2009).

[CrossRef]

H. S. Chang, Y. Weiss, and W. T. Freeman, “Informative sensing,” submitted to IEEE Trans. Inf. Theory, preprint available at arXiv http://arxiv.org/abs/0901.4275 (2009).

L. He and L. Carin, “Exploiting structure in wavelet-based Bayesian compressive sensing,” IEEE Trans. Signal Process. 57, 3488–3497 (2009).

[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. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[CrossRef]

S. Ji and L. Carin, “Bayesian compressive sensing and projection optimization,” in Proceedings of the 24th International Conference on Machine Learning (ICML) (ACM, 2007), pp. 377–384.

F. Krahmer and R. Ward, “New and improved Johnson–Lindenstrauss embeddings via the restricted isometry property,” SIAM J. Math. Anal.43, 1269–1281 (2010).

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vol. 19 (MIT, 2007), pp. 801–808.

D. Taubman and M. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice (Kluwer, 2001).

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2012).

[CrossRef]

Y. C. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Trans. Inf. Theory 55, 5302–5316 (2009).

[CrossRef]

A. Ashok and M. A. Neifeld, “Compressive imaging: hybrid measurement basis design,” J. Opt. Soc. Am. A 28, 1041–1050 (2011).

[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging system design using task-specific information,” Appl. Opt. 47, 4457–4471 (2008).

[CrossRef]

M. A. Neifeld, A. Ashok, and P. K. Baheti, “Task-specific information for imaging system analysis,” J. Opt. Soc. Am. A 24, B25–B41 (2007).

[CrossRef]

M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293–5303 (2007).

[CrossRef]

H. Pal and M. A. Neifeld, “Multispectral principal component imaging,” Opt. Express 11, 2118–2125 (2003).

[CrossRef]

M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379–3389 (2003).

[CrossRef]

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vol. 19 (MIT, 2007), pp. 801–808.

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2012).

[CrossRef]

M. W. Seeger and H. Nickisch, “Compressed sensing and Bayesian experimental design,” in Proceedings of the 25th International Conference on Machine Learning (ICML) (ACM, 2008), pp. 912–919.

H. Nyquist, “Certain topics in telegraph transmission theory,” Trans. Am. Inst. Electr. Eng. 47, 617–644 (1928).

[CrossRef]

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

D. Stowell and M. D. Plumbley, “Fast multidimensional entropy estimation by k-d partitioning,” IEEE Signal Process. Lett. 16, 537–540 (2009).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

W. Chen and W. Pratt, “Scene adaptive coder,” IEEE Trans. Commun. 32, 225–232 (1984).

[CrossRef]

G. Puy, P. Vandergheynst, and Y. Wiaux, “On variable density compressive sampling,” IEEE Signal Process. Lett. 18, 595–598 (2011).

[CrossRef]

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vol. 19 (MIT, 2007), pp. 801–808.

H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory 54, 2210–2219 (2008).

[CrossRef]

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

J. Romberg, “Imaging via compressing sampling,” IEEE Signal Process. Mag. 25, (2) 14–20 (2008).

[CrossRef]

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

[CrossRef]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5674, 76–86 (2005).

[CrossRef]

D. L. Ruderman, “Origins of scaling in natural images,” Vis. Res. 37, 3385–3398 (1997).

[CrossRef]

H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory 54, 2210–2219 (2008).

[CrossRef]

M. W. Seeger and H. Nickisch, “Compressed sensing and Bayesian experimental design,” in Proceedings of the 25th International Conference on Machine Learning (ICML) (ACM, 2008), pp. 912–919.

C. E. Shannon, “Communication in the presence of noise,” Proc. IRE 37, 10–21 (1949).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).

[CrossRef]

D. Stowell and M. D. Plumbley, “Fast multidimensional entropy estimation by k-d partitioning,” IEEE Signal Process. Lett. 16, 537–540 (2009).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

D. J. Tolhurst, Y. Tadmor, and T. Chao, “Amplitude spectra of natural images,” Ophthalmic Physiolog. Opt. 12, 229–232 (1992).

[CrossRef]

E. Candes and T. Tal, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).

[CrossRef]

M. A. Tanner, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd ed. (Springer-Verlag, 1996).

E. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51, 4203–4215 (2005).

[CrossRef]

D. Taubman and M. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice (Kluwer, 2001).

D. J. Tolhurst, Y. Tadmor, and T. Chao, “Amplitude spectra of natural images,” Ophthalmic Physiolog. Opt. 12, 229–232 (1992).

[CrossRef]

Y. Tsaig and D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).

[CrossRef]

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).

[CrossRef]

G. Puy, P. Vandergheynst, and Y. Wiaux, “On variable density compressive sampling,” IEEE Signal Process. Lett. 18, 595–598 (2011).

[CrossRef]

H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory 54, 2210–2219 (2008).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 30–44 (1991).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

F. Krahmer and R. Ward, “New and improved Johnson–Lindenstrauss embeddings via the restricted isometry property,” SIAM J. Math. Anal.43, 1269–1281 (2010).

H. S. Chang, Y. Weiss, and W. T. Freeman, “Informative sensing,” submitted to IEEE Trans. Inf. Theory, preprint available at arXiv http://arxiv.org/abs/0901.4275 (2009).

E. T. Whittaker, “On the functions which are represented by the expansions of the interpolation theory,” Proc. R. Soc. Edinburgh 35, 181–194 (1915).

G. Puy, P. Vandergheynst, and Y. Wiaux, “On variable density compressive sampling,” IEEE Signal Process. Lett. 18, 595–598 (2011).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2012).

[CrossRef]

Y. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[CrossRef]

Y. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[CrossRef]

Y. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[CrossRef]

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).

[CrossRef]

M. A. Neifeld and P. Shankar, “Feature-specific imaging,” Appl. Opt. 42, 3379–3389 (2003).

[CrossRef]

M. A. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt. 46, 5293–5303 (2007).

[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Compressive imaging system design using task-specific information,” Appl. Opt. 47, 4457–4471 (2008).

[CrossRef]

G. Wallace, “The JPEG still picture compression standard,” Commun. ACM 34, 30–44 (1991).

[CrossRef]

D. Stowell and M. D. Plumbley, “Fast multidimensional entropy estimation by k-d partitioning,” IEEE Signal Process. Lett. 16, 537–540 (2009).

[CrossRef]

G. Puy, P. Vandergheynst, and Y. Wiaux, “On variable density compressive sampling,” IEEE Signal Process. Lett. 18, 595–598 (2011).

[CrossRef]

J. Romberg, “Imaging via compressing sampling,” IEEE Signal Process. Mag. 25, (2) 14–20 (2008).

[CrossRef]

W. Chen and W. Pratt, “Scene adaptive coder,” IEEE Trans. Commun. 32, 225–232 (1984).

[CrossRef]

J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Trans. Image Process. 12, 1338–1351 (2003).

[CrossRef]

Y. Yang, J. Wright, T. S. Huang, and M. Yi, “Image super-resolution via sparse representation,” IEEE Trans. Image Process. 19, 2861–2873 (2010).

[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. Tsaig and D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).

[CrossRef]

E. Candes and T. Tao, “Decoding by linear programming,” IEEE Trans. Inf. Theory 51, 4203–4215 (2005).

[CrossRef]

H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory 54, 2210–2219 (2008).

[CrossRef]

E. Candes and T. Tal, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inf. Theory 52, 5406–5425 (2006).

[CrossRef]

Y. C. Eldar and M. Mishali, “Robust recovery of signals from a structured union of subspaces,” IEEE Trans. Inf. Theory 55, 5302–5316 (2009).

[CrossRef]

M. Chen, J. Silva, J. Paisley, C. Wang, D. Dunson, and L. Carin, “Compressive sensing on manifolds using a nonparametric mixture of factor analyzers: algorithm and performance bounds,” IEEE Trans. Signal Process. 58, 6140–6155 (2010).

[CrossRef]

L. He and L. Carin, “Exploiting structure in wavelet-based Bayesian compressive sensing,” IEEE Trans. Signal Process. 57, 3488–3497 (2009).

[CrossRef]

M. Elad, “Optimized projections for compressed sensing,” IEEE Trans. Signal Process. 55, 5695–5702 (2007).

[CrossRef]

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

[CrossRef]

D. J. Tolhurst, Y. Tadmor, and T. Chao, “Amplitude spectra of natural images,” Ophthalmic Physiolog. Opt. 12, 229–232 (1992).

[CrossRef]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50, 072601 (2012).

[CrossRef]

C. E. Shannon, “Communication in the presence of noise,” Proc. IRE 37, 10–21 (1949).

[CrossRef]

E. T. Whittaker, “On the functions which are represented by the expansions of the interpolation theory,” Proc. R. Soc. Edinburgh 35, 181–194 (1915).

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5674, 76–86 (2005).

[CrossRef]

D. Donoho and Y. Tsaig, “Extensions of compressed sensing,” Signal Process. 86, 549–571 (2006).

[CrossRef]

H. Nyquist, “Certain topics in telegraph transmission theory,” Trans. Am. Inst. Electr. Eng. 47, 617–644 (1928).

[CrossRef]

D. L. Ruderman, “Origins of scaling in natural images,” Vis. Res. 37, 3385–3398 (1997).

[CrossRef]

USC-SIPI Image Database. Available at http://sipi.usc.edu/database .

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of Advances in Neural Information Processing Systems (NIPS), Vol. 19 (MIT, 2007), pp. 801–808.

M. A. Tanner, Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd ed. (Springer-Verlag, 1996).

S. Ji and L. Carin, “Bayesian compressive sensing and projection optimization,” in Proceedings of the 24th International Conference on Machine Learning (ICML) (ACM, 2007), pp. 377–384.

M. W. Seeger and H. Nickisch, “Compressed sensing and Bayesian experimental design,” in Proceedings of the 25th International Conference on Machine Learning (ICML) (ACM, 2008), pp. 912–919.

H. S. Chang, Y. Weiss, and W. T. Freeman, “Informative sensing,” submitted to IEEE Trans. Inf. Theory, preprint available at arXiv http://arxiv.org/abs/0901.4275 (2009).

W. R. Carson, M. Chen, M. R. D. Rodrigues, R. Calderbank, and L. Carin, “Communications-inspired projection design with application to compressive sensing,” preprint available at arXiv:1206.1973 http://arxiv.org/abs/1206.1973 (2012).

D. Taubman and M. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice (Kluwer, 2001).

F. Krahmer and R. Ward, “New and improved Johnson–Lindenstrauss embeddings via the restricted isometry property,” SIAM J. Math. Anal.43, 1269–1281 (2010).