P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

M. Shankar, N. Pitsianis, and D. Brady, “Compressive video sensors using multichannel imagers,” Appl. Opt. 49, B9–B17 (2010).

[Crossref]
[PubMed]

D. Kittle, K. Choi, A. Wagadarikar, and D. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).

[Crossref]
[PubMed]

J. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).

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

[Crossref]

M. Elad and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process. 17, 53–69 (2008).

[Crossref]
[PubMed]

H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[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]
[PubMed]

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[Crossref]

R. Tibshirani, “Regression shrinkage and selection via the Lasso,” J. R. Stat. Soc. Ser. B 58, 267–288 (1996).

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]
[PubMed]

S. Bourguignon, D. Mary, and E. Slezak, “Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE, 2010), 1–4.

[Crossref]

M. Shankar, N. Pitsianis, and D. Brady, “Compressive video sensors using multichannel imagers,” Appl. Opt. 49, B9–B17 (2010).

[Crossref]
[PubMed]

D. Kittle, K. Choi, A. Wagadarikar, and D. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).

[Crossref]
[PubMed]

A. Wagadarikar, N. Pitsianis, X. Sun, and D. Brady, “Video rate spectral imaging using a coded aperture snapshot spectral imager,” Opt. Express 17, 6368–6388 (2009).

[Crossref]
[PubMed]

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]

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[Crossref]

M. Elad and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process. 17, 53–69 (2008).

[Crossref]
[PubMed]

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

[Crossref]
[PubMed]

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]

G. Farnebäck, “Two-frame motion estimation based on polynomial expansion,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), 363–370.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[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]

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[Crossref]

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

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

[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. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).

S. Bourguignon, D. Mary, and E. Slezak, “Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE, 2010), 1–4.

[Crossref]

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

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

[Crossref]

M. Shankar, N. Pitsianis, and D. Brady, “Compressive video sensors using multichannel imagers,” Appl. Opt. 49, B9–B17 (2010).

[Crossref]
[PubMed]

A. Wagadarikar, N. Pitsianis, X. Sun, and D. Brady, “Video rate spectral imaging using a coded aperture snapshot spectral imager,” Opt. Express 17, 6368–6388 (2009).

[Crossref]
[PubMed]

J. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).

H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).

[Crossref]

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

[Crossref]

J. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).

M. Elad and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process. 17, 53–69 (2008).

[Crossref]
[PubMed]

S. Bourguignon, D. Mary, and E. Slezak, “Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE, 2010), 1–4.

[Crossref]

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[Crossref]

R. Tibshirani, “Regression shrinkage and selection via the Lasso,” J. R. Stat. Soc. Ser. B 58, 267–288 (1996).

D. Kittle, K. Choi, A. Wagadarikar, and D. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).

[Crossref]
[PubMed]

A. Wagadarikar, N. Pitsianis, X. Sun, and D. Brady, “Video rate spectral imaging using a coded aperture snapshot spectral imager,” Opt. Express 17, 6368–6388 (2009).

[Crossref]
[PubMed]

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

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]

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat. 32, 407–499 (2004).

[Crossref]

M. Shankar, N. Pitsianis, and D. Brady, “Compressive video sensors using multichannel imagers,” Appl. Opt. 49, B9–B17 (2010).

[Crossref]
[PubMed]

D. Kittle, K. Choi, A. Wagadarikar, and D. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).

[Crossref]
[PubMed]

H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (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]
[PubMed]

M. Elad and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process. 17, 53–69 (2008).

[Crossref]
[PubMed]

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

P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process. 5, 423–434 (2011).

J. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).

R. Tibshirani, “Regression shrinkage and selection via the Lasso,” J. R. Stat. Soc. Ser. B 58, 267–288 (1996).

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

[Crossref]

S. Bourguignon, D. Mary, and E. Slezak, “Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE, 2010), 1–4.

[Crossref]

G. Farnebäck, “Two-frame motion estimation based on polynomial expansion,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), 363–370.