R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” Lect. Notes Comput. Sci. 6920/2012, 711–730 (2012).

[CrossRef]

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

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process. 58, 1553–1564 (2010).

[CrossRef]

K. Skretting, and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Trans. Signal Process. 58, 2121–2130 (2010).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev. 51, 34–81 (2009).

[CrossRef]

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

[CrossRef]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).

[CrossRef]

M. Elad, and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

J.-L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).

[CrossRef]

H. Krim, D. Tucker, and S. Mallat, “On denoising and best signal representation,” IEEE Trans. Inf. Theory 45, 2225–2238 (1999).

[CrossRef]

R. Neff, and A. Zakhor, “Very low bit-rate video coding based on matching pursuits,” IEEE Trans. Circuits Syst. Video Technol. 7, 158–171 (1997).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

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

[CrossRef]

S. G. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).

[CrossRef]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).

[CrossRef]

M. Elad, and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (2005), Vol. 5, pp. 293–296.

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (2005), Vol. 5, pp. 293–296.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev. 51, 34–81 (2009).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

J.-L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev. 51, 34–81 (2009).

[CrossRef]

J.-L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).

[CrossRef]

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

[CrossRef]

R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” Lect. Notes Comput. Sci. 6920/2012, 711–730 (2012).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process. 58, 1553–1564 (2010).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev. 51, 34–81 (2009).

[CrossRef]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).

[CrossRef]

M. Elad, and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Springer, 2010).

R. Rubinstein, M. Zibulevsky, and M. Elad, “Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit,” CS Technical Report (Technion—Israel Institute of Technology, 2008).

K. Skretting, and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Trans. Signal Process. 58, 2121–2130 (2010).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

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

[CrossRef]

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (2005), Vol. 5, pp. 293–296.

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

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

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

H. Krim, D. Tucker, and S. Mallat, “On denoising and best signal representation,” IEEE Trans. Inf. Theory 45, 2225–2238 (1999).

[CrossRef]

Y. Pati, R. Rezaifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in 1993 Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers (1993), Vol. 1, pp. 40–44.

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (2005), Vol. 5, pp. 293–296.

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

[CrossRef]

H. Krim, D. Tucker, and S. Mallat, “On denoising and best signal representation,” IEEE Trans. Inf. Theory 45, 2225–2238 (1999).

[CrossRef]

S. G. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

R. Neff, and A. Zakhor, “Very low bit-rate video coding based on matching pursuits,” IEEE Trans. Circuits Syst. Video Technol. 7, 158–171 (1997).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

Y. Pati, R. Rezaifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in 1993 Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers (1993), Vol. 1, pp. 40–44.

R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” Lect. Notes Comput. Sci. 6920/2012, 711–730 (2012).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

Y. Pati, R. Rezaifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in 1993 Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers (1993), Vol. 1, pp. 40–44.

R. Rubinstein, M. Zibulevsky, and M. Elad, “double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process. 58, 1553–1564 (2010).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit,” CS Technical Report (Technion—Israel Institute of Technology, 2008).

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

K. Skretting, and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Trans. Signal Process. 58, 2121–2130 (2010).

[CrossRef]

J.-L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).

[CrossRef]

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

[CrossRef]

H. Krim, D. Tucker, and S. Mallat, “On denoising and best signal representation,” IEEE Trans. Inf. Theory 45, 2225–2238 (1999).

[CrossRef]

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

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

[CrossRef]

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

[CrossRef]

R. Neff, and A. Zakhor, “Very low bit-rate video coding based on matching pursuits,” IEEE Trans. Circuits Syst. Video Technol. 7, 158–171 (1997).

[CrossRef]

R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” Lect. Notes Comput. Sci. 6920/2012, 711–730 (2012).

[CrossRef]

S. G. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process. 58, 1553–1564 (2010).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit,” CS Technical Report (Technion—Israel Institute of Technology, 2008).

M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett. 3, 10–12 (1996).

[CrossRef]

R. Neff, and A. Zakhor, “Very low bit-rate video coding based on matching pursuits,” IEEE Trans. Circuits Syst. Video Technol. 7, 158–171 (1997).

[CrossRef]

J.-L. Starck, E. J. Candes, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).

[CrossRef]

M. Elad, and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15, 3736–3745 (2006).

[CrossRef]

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

[CrossRef]

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

[CrossRef]

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

[CrossRef]

H. Krim, D. Tucker, and S. Mallat, “On denoising and best signal representation,” IEEE Trans. Inf. Theory 45, 2225–2238 (1999).

[CrossRef]

K. Skretting, and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Trans. Signal Process. 58, 2121–2130 (2010).

[CrossRef]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).

[CrossRef]

R. Rubinstein, M. Zibulevsky, and M. Elad, “double sparsity: learning sparse dictionaries for sparse signal approximation,” IEEE Trans. Signal Process. 58, 1553–1564 (2010).

[CrossRef]

S. G. Mallat, and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process. 41, 3397–3415 (1993).

[CrossRef]

R. Zeyde, M. Elad, and M. Protter, “On single image scale-up using sparse-representations,” Lect. Notes Comput. Sci. 6920/2012, 711–730 (2012).

[CrossRef]

K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. Lee, and T. J. Sejnowski, “Dictionary learning algorithms for sparse representations,” Neural Comput. 15, 349–396 (2003).

[CrossRef]

A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev. 51, 34–81 (2009).

[CrossRef]

S. Lesage, R. Gribonval, F. Bimbot, and L. Benaroya, “Learning unions of orthonormal bases with thresholded singular value decomposition,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (2005), Vol. 5, pp. 293–296.

R. Rubinstein, M. Zibulevsky, and M. Elad, “Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit,” CS Technical Report (Technion—Israel Institute of Technology, 2008).

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Springer, 2010).

Y. Pati, R. Rezaifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in 1993 Conference Record of the 27th Asilomar Conference on Signals, Systems and Computers (1993), Vol. 1, pp. 40–44.