P. Shankar, W. Hassenplaugh, R. Morrison, R. Stack, and M. Neifeld, “Multiaperture imaging,” Appl. Opt. 45, 2871-2883 (2006).

[CrossRef]
[PubMed]

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207-1223 (2006).

[CrossRef]

E. Candes, 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]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5678, 76-86 (2005).

[CrossRef]

N. Valdivia and E. Williams, “Krylov subspace iterative methods for boundary element method based near-field acoustic holography,” J. Acoust. Soc. Am. 117, 711-724 (2005).

[CrossRef]
[PubMed]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413-1457 (2004).

[CrossRef]

D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Process. 52, 2153-2164 (2004).

[CrossRef]

M. Figueiredo and R. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906916 (2003).

[CrossRef]

S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002).

[CrossRef]

A. Fruchter and R. Hook, “Drizzle: a method for the linear reconstruction of undersampled images,” Publ. Astron. Soc. Pac. 114, 144-152 (2002).

[CrossRef]

M. Kilmer and D. O'Leary, “Choosing regularization parameters in iterative methods for ill-posed problems,” SIAM J. Matrix Anal. Appl. 22, 1204-1221 (2001).

[CrossRef]

M. Belge, M. Kilmer, and E. Miller. “Wavelet domain image restoration with adaptive edge-preserving regularization,” IEEE Trans. Image Process. 9, 597-608 (2000).

[CrossRef]

B. Rao and K. Kreutz-Delgado, “An affine scaling methodology for best basis selection,” IEEE Trans. Signal Process. 47, 187-200 (1999).

[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. (USA) 20, 33-61 (1998).

[CrossRef]

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

R. Hardie, K. Bernard, and E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621-1633 (1997).

[CrossRef]
[PubMed]

D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613-627 (1995).

[CrossRef]

B. Jeffs and M. Gunsay, “Restoration of blurred star field images by maximally sparse optimization,” IEEE Trans. Image Process. 2, 202-211 (1993).

[CrossRef]
[PubMed]

P. Hansen, “Analysis of discrete ill-posed problems by means of the L-curve,” SIAM Rev. 34, 561-580 (1992).

[CrossRef]

M. Irani and S. Peleg, “Improving resolution by image registration,” Comput. Vis. Graph. Image Process. 53, 231-239 (1991).

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693 (1989).

[CrossRef]

A. Goshtasby, “Image registration by local approximation methods,” Image Vis. Comput. 6, 255-261 (1988).

[CrossRef]

M. Figueiredo, R. Nowak, and S. Wright, “Gradient projections for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 4, 586-597 (1987).

R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing 1, 317-339 (1984).

C. Paige and M. Saunders, “LSQR: An algorithm for sparse linear equations and sparse least squares,” ACM Trans. Math. Softw. 8, 43-71 (1982).

[CrossRef]

C. Paige and M. Saunders, “LSQR: Sparse linear equations and least squares problems,” ACM Trans. Math. Softw. 8, 195-209 (1982).

[CrossRef]

J. Mannos and D. Sakrison, “The effects of visual fidelity criterion on the encoding of image,” IRE Trans. Inf. Theory 20, 525-536 (1974).

[CrossRef]

K. Aizawa, T. Komatsu, and T. Saito, “A scheme for acquiring very high resolution images using multiple cameras,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1992), Vol. 3, pp. 23-26.

H. Andrews and B. Hunt, Digital Image Restoration (Prentice-Hall, 1977).

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

R. Hardie, K. Bernard, and E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621-1633 (1997).

[CrossRef]
[PubMed]

S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002).

[CrossRef]

M. Duarte, M. Wakin, and R. Baraniuk, “Fast reconstruction of piecewise smooth signals from random projections,” Online Proceedings of the Workshop on Signal Processing with Adaptative Sparse Structured Representations, SPARS 2005, http://spars05.irisa.fr/ACTES/TS5-3.pdf.

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

H. Barrett and K. Myers, Foundations of Image Science (Wiley Series in Pure and Applied Optics, 2004).

M. Belge, M. Kilmer, and E. Miller. “Wavelet domain image restoration with adaptive edge-preserving regularization,” IEEE Trans. Image Process. 9, 597-608 (2000).

[CrossRef]

R. Hardie, K. Bernard, and E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621-1633 (1997).

[CrossRef]
[PubMed]

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

E. Cands, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Technical Report, California Institute of Technology, http://www.acm.caltech.edu/emmanuel/papers/rwll-oct2007.pdf.

G. Harikumar and Y. Bresler, “A new algorithm for computing sparse solutions to linear inverse problems,” in Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 1996), Vol. 3, pp. 1131-1334.

E. Candes, 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]

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207-1223 (2006).

[CrossRef]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5678, 76-86 (2005).

[CrossRef]

E. Cands, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Technical Report, California Institute of Technology, http://www.acm.caltech.edu/emmanuel/papers/rwll-oct2007.pdf.

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. (USA) 20, 33-61 (1998).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413-1457 (2004).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413-1457 (2004).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413-1457 (2004).

[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. (USA) 20, 33-61 (1998).

[CrossRef]

D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613-627 (1995).

[CrossRef]

M. Duarte, M. Wakin, and R. Baraniuk, “Fast reconstruction of piecewise smooth signals from random projections,” Online Proceedings of the Workshop on Signal Processing with Adaptative Sparse Structured Representations, SPARS 2005, http://spars05.irisa.fr/ACTES/TS5-3.pdf.

M. Figueiredo and R. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906916 (2003).

[CrossRef]

M. Figueiredo, R. Nowak, and S. Wright, “Gradient projections for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 4, 586-597 (1987).

A. Fruchter and R. Hook, “Drizzle: a method for the linear reconstruction of undersampled images,” Publ. Astron. Soc. Pac. 114, 144-152 (2002).

[CrossRef]

N. Nguyen, G. Golub, and P. Milanfar, “Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules,” in Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Calif., October 1999, pp. 1257-1261.

A. Goshtasby, “Image registration by local approximation methods,” Image Vis. Comput. 6, 255-261 (1988).

[CrossRef]

B. Jeffs and M. Gunsay, “Restoration of blurred star field images by maximally sparse optimization,” IEEE Trans. Image Process. 2, 202-211 (1993).

[CrossRef]
[PubMed]

P. Hansen, “Analysis of discrete ill-posed problems by means of the L-curve,” SIAM Rev. 34, 561-580 (1992).

[CrossRef]

P. Hansen, Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion (SIAM, 1998).

[CrossRef]

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

R. Hardie, K. Bernard, and E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621-1633 (1997).

[CrossRef]
[PubMed]

G. Harikumar and Y. Bresler, “A new algorithm for computing sparse solutions to linear inverse problems,” in Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 1996), Vol. 3, pp. 1131-1334.

A. Fruchter and R. Hook, “Drizzle: a method for the linear reconstruction of undersampled images,” Publ. Astron. Soc. Pac. 114, 144-152 (2002).

[CrossRef]

R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing 1, 317-339 (1984).

H. Andrews and B. Hunt, Digital Image Restoration (Prentice-Hall, 1977).

M. Irani and S. Peleg, “Improving resolution by image registration,” Comput. Vis. Graph. Image Process. 53, 231-239 (1991).

B. Jeffs and M. Gunsay, “Restoration of blurred star field images by maximally sparse optimization,” IEEE Trans. Image Process. 2, 202-211 (1993).

[CrossRef]
[PubMed]

M. Jiang, L. Xia, G. Shou, and M. Tang, “Combination of the LSQR method and a genetic algorithm for solving the electrocardiography inverse problem,” Phys. Med. Biol. 52, 1277-1294 (2007).

[CrossRef]
[PubMed]

S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002).

[CrossRef]

M. Kilmer and D. O'Leary, “Choosing regularization parameters in iterative methods for ill-posed problems,” SIAM J. Matrix Anal. Appl. 22, 1204-1221 (2001).

[CrossRef]

M. Belge, M. Kilmer, and E. Miller. “Wavelet domain image restoration with adaptive edge-preserving regularization,” IEEE Trans. Image Process. 9, 597-608 (2000).

[CrossRef]

K. Aizawa, T. Komatsu, and T. Saito, “A scheme for acquiring very high resolution images using multiple cameras,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1992), Vol. 3, pp. 23-26.

B. Rao and K. Kreutz-Delgado, “An affine scaling methodology for best basis selection,” IEEE Trans. Signal Process. 47, 187-200 (1999).

[CrossRef]

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693 (1989).

[CrossRef]

S. Mallat, “A compact multiresolution representation: the wavelet model,” presented at the IEEE Workshop Computer Society on Computer Vision, Miami, Florida, December 2-7, 1987.

J. Mannos and D. Sakrison, “The effects of visual fidelity criterion on the encoding of image,” IRE Trans. Inf. Theory 20, 525-536 (1974).

[CrossRef]

N. Nguyen, G. Golub, and P. Milanfar, “Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules,” in Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Calif., October 1999, pp. 1257-1261.

M. Belge, M. Kilmer, and E. Miller. “Wavelet domain image restoration with adaptive edge-preserving regularization,” IEEE Trans. Image Process. 9, 597-608 (2000).

[CrossRef]

H. Barrett and K. Myers, Foundations of Image Science (Wiley Series in Pure and Applied Optics, 2004).

P. Shankar and M. Neifeld, “Multiframe superresolution of binary images,” Appl. Opt. 46, 1211-1222 (2007).

[CrossRef]
[PubMed]

P. Shankar, W. Hassenplaugh, R. Morrison, R. Stack, and M. Neifeld, “Multiaperture imaging,” Appl. Opt. 45, 2871-2883 (2006).

[CrossRef]
[PubMed]

N. Nguyen, G. Golub, and P. Milanfar, “Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules,” in Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Calif., October 1999, pp. 1257-1261.

M. Figueiredo and R. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906916 (2003).

[CrossRef]

M. Figueiredo, R. Nowak, and S. Wright, “Gradient projections for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 4, 586-597 (1987).

M. Kilmer and D. O'Leary, “Choosing regularization parameters in iterative methods for ill-posed problems,” SIAM J. Matrix Anal. Appl. 22, 1204-1221 (2001).

[CrossRef]

C. Paige and M. Saunders, “LSQR: Sparse linear equations and least squares problems,” ACM Trans. Math. Softw. 8, 195-209 (1982).

[CrossRef]

C. Paige and M. Saunders, “LSQR: An algorithm for sparse linear equations and sparse least squares,” ACM Trans. Math. Softw. 8, 43-71 (1982).

[CrossRef]

M. Irani and S. Peleg, “Improving resolution by image registration,” Comput. Vis. Graph. Image Process. 53, 231-239 (1991).

M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in IEEE Proceedings on Computer Vision and Pattern Recognition (IEEE, 1991), pp. 586-591.

[CrossRef]

D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Process. 52, 2153-2164 (2004).

[CrossRef]

B. Rao and K. Kreutz-Delgado, “An affine scaling methodology for best basis selection,” IEEE Trans. Signal Process. 47, 187-200 (1999).

[CrossRef]

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207-1223 (2006).

[CrossRef]

E. Candes, 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]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5678, 76-86 (2005).

[CrossRef]

K. Aizawa, T. Komatsu, and T. Saito, “A scheme for acquiring very high resolution images using multiple cameras,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 1992), Vol. 3, pp. 23-26.

J. Mannos and D. Sakrison, “The effects of visual fidelity criterion on the encoding of image,” IRE Trans. Inf. Theory 20, 525-536 (1974).

[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. (USA) 20, 33-61 (1998).

[CrossRef]

C. Paige and M. Saunders, “LSQR: An algorithm for sparse linear equations and sparse least squares,” ACM Trans. Math. Softw. 8, 43-71 (1982).

[CrossRef]

C. Paige and M. Saunders, “LSQR: Sparse linear equations and least squares problems,” ACM Trans. Math. Softw. 8, 195-209 (1982).

[CrossRef]

P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, “How to take advantage of aliasing in bandlimited signals,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (IEEE, 2004), pp. 948-951.

P. Shankar and M. Neifeld, “Multiframe superresolution of binary images,” Appl. Opt. 46, 1211-1222 (2007).

[CrossRef]
[PubMed]

P. Shankar, W. Hassenplaugh, R. Morrison, R. Stack, and M. Neifeld, “Multiaperture imaging,” Appl. Opt. 45, 2871-2883 (2006).

[CrossRef]
[PubMed]

M. Jiang, L. Xia, G. Shou, and M. Tang, “Combination of the LSQR method and a genetic algorithm for solving the electrocardiography inverse problem,” Phys. Med. Biol. 52, 1277-1294 (2007).

[CrossRef]
[PubMed]

M. Jiang, L. Xia, G. Shou, and M. Tang, “Combination of the LSQR method and a genetic algorithm for solving the electrocardiography inverse problem,” Phys. Med. Biol. 52, 1277-1294 (2007).

[CrossRef]
[PubMed]

E. Candes, 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]

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207-1223 (2006).

[CrossRef]

R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing 1, 317-339 (1984).

M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in IEEE Proceedings on Computer Vision and Pattern Recognition (IEEE, 1991), pp. 586-591.

[CrossRef]

N. Valdivia and E. Williams, “Krylov subspace iterative methods for boundary element method based near-field acoustic holography,” J. Acoust. Soc. Am. 117, 711-724 (2005).

[CrossRef]
[PubMed]

P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, “How to take advantage of aliasing in bandlimited signals,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (IEEE, 2004), pp. 948-951.

P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, “How to take advantage of aliasing in bandlimited signals,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (IEEE, 2004), pp. 948-951.

P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, “How to take advantage of aliasing in bandlimited signals,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (IEEE, 2004), pp. 948-951.

E. Cands, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Technical Report, California Institute of Technology, http://www.acm.caltech.edu/emmanuel/papers/rwll-oct2007.pdf.

M. Duarte, M. Wakin, and R. Baraniuk, “Fast reconstruction of piecewise smooth signals from random projections,” Online Proceedings of the Workshop on Signal Processing with Adaptative Sparse Structured Representations, SPARS 2005, http://spars05.irisa.fr/ACTES/TS5-3.pdf.

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

N. Valdivia and E. Williams, “Krylov subspace iterative methods for boundary element method based near-field acoustic holography,” J. Acoust. Soc. Am. 117, 711-724 (2005).

[CrossRef]
[PubMed]

D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Process. 52, 2153-2164 (2004).

[CrossRef]

M. Figueiredo, R. Nowak, and S. Wright, “Gradient projections for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 4, 586-597 (1987).

M. Jiang, L. Xia, G. Shou, and M. Tang, “Combination of the LSQR method and a genetic algorithm for solving the electrocardiography inverse problem,” Phys. Med. Biol. 52, 1277-1294 (2007).

[CrossRef]
[PubMed]

C. Paige and M. Saunders, “LSQR: An algorithm for sparse linear equations and sparse least squares,” ACM Trans. Math. Softw. 8, 43-71 (1982).

[CrossRef]

C. Paige and M. Saunders, “LSQR: Sparse linear equations and least squares problems,” ACM Trans. Math. Softw. 8, 195-209 (1982).

[CrossRef]

R. Tsai and T. Huang, “Multiframe image restoration and registration,” Advances in Computer Vision and Image Processing 1, 317-339 (1984).

P. Shankar, W. Hassenplaugh, R. Morrison, R. Stack, and M. Neifeld, “Multiaperture imaging,” Appl. Opt. 45, 2871-2883 (2006).

[CrossRef]
[PubMed]

P. Shankar and M. Neifeld, “Multiframe superresolution of binary images,” Appl. Opt. 46, 1211-1222 (2007).

[CrossRef]
[PubMed]

E. Candes, J. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207-1223 (2006).

[CrossRef]

I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413-1457 (2004).

[CrossRef]

M. Irani and S. Peleg, “Improving resolution by image registration,” Comput. Vis. Graph. Image Process. 53, 231-239 (1991).

M. Figueiredo, R. Nowak, and S. Wright, “Gradient projections for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 4, 586-597 (1987).

M. Figueiredo and R. Nowak, “An EM algorithm for wavelet-based image restoration,” IEEE Trans. Image Process. 12, 906916 (2003).

[CrossRef]

R. Hardie, K. Bernard, and E. Armstrong, “Joint MAP registration and high-resolution image estimation using a sequence of undersampled images,” IEEE Trans. Image Process. 6, 1621-1633 (1997).

[CrossRef]
[PubMed]

M. Belge, M. Kilmer, and E. Miller. “Wavelet domain image restoration with adaptive edge-preserving regularization,” IEEE Trans. Image Process. 9, 597-608 (2000).

[CrossRef]

B. Jeffs and M. Gunsay, “Restoration of blurred star field images by maximally sparse optimization,” IEEE Trans. Image Process. 2, 202-211 (1993).

[CrossRef]
[PubMed]

D. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613-627 (1995).

[CrossRef]

E. Candes, 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]

S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693 (1989).

[CrossRef]

S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1167-1183 (2002).

[CrossRef]

D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Process. 52, 2153-2164 (2004).

[CrossRef]

B. Rao and K. Kreutz-Delgado, “An affine scaling methodology for best basis selection,” IEEE Trans. Signal Process. 47, 187-200 (1999).

[CrossRef]

A. Goshtasby, “Image registration by local approximation methods,” Image Vis. Comput. 6, 255-261 (1988).

[CrossRef]

J. Mannos and D. Sakrison, “The effects of visual fidelity criterion on the encoding of image,” IRE Trans. Inf. Theory 20, 525-536 (1974).

[CrossRef]

N. Valdivia and E. Williams, “Krylov subspace iterative methods for boundary element method based near-field acoustic holography,” J. Acoust. Soc. Am. 117, 711-724 (2005).

[CrossRef]
[PubMed]

R. Hardie, K. Barnard, J. Bognar, E. Armstrong, and E. Watson, “High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system,” Opt. Eng. (Bellingham) 37, 247-260 (1998).

[CrossRef]

M. Jiang, L. Xia, G. Shou, and M. Tang, “Combination of the LSQR method and a genetic algorithm for solving the electrocardiography inverse problem,” Phys. Med. Biol. 52, 1277-1294 (2007).

[CrossRef]
[PubMed]

E. Candes and J. Romberg, “Signal recovery from random projections,” Proc. SPIE 5678, 76-86 (2005).

[CrossRef]

A. Fruchter and R. Hook, “Drizzle: a method for the linear reconstruction of undersampled images,” Publ. Astron. Soc. Pac. 114, 144-152 (2002).

[CrossRef]

M. Kilmer and D. O'Leary, “Choosing regularization parameters in iterative methods for ill-posed problems,” SIAM J. Matrix Anal. Appl. 22, 1204-1221 (2001).

[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput. (USA) 20, 33-61 (1998).

[CrossRef]

P. Hansen, “Analysis of discrete ill-posed problems by means of the L-curve,” SIAM Rev. 34, 561-580 (1992).

[CrossRef]

S. Mallat, “A compact multiresolution representation: the wavelet model,” presented at the IEEE Workshop Computer Society on Computer Vision, Miami, Florida, December 2-7, 1987.

USC-SIPI image database, Signal and Image Processing Institute at the University of Southern California, http://sipi.usc.edu/database.

G. Harikumar and Y. Bresler, “A new algorithm for computing sparse solutions to linear inverse problems,” in Proceedings of the 1996 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, 1996), Vol. 3, pp. 1131-1334.

H. Andrews and B. Hunt, Digital Image Restoration (Prentice-Hall, 1977).

P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, “How to take advantage of aliasing in bandlimited signals,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing (IEEE, 2004), pp. 948-951.

N. Nguyen, G. Golub, and P. Milanfar, “Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules,” in Proceedings of the 33rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, Calif., October 1999, pp. 1257-1261.

E. Cands, M. Wakin, and S. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Technical Report, California Institute of Technology, http://www.acm.caltech.edu/emmanuel/papers/rwll-oct2007.pdf.

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