Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

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

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

[CrossRef]

R. Perkins and V. Gruev, “Signal-to-noise analysis of Stokes parameters in division of focal plane polarimeters,” Opt. Express 18, 25815–25824 (2010).

[CrossRef]

A. Bénière, F. Goudail, M. Alouini, and D. Dolfi, “Degree of polarization estimation in the presence of nonuniform illumination and additive Gaussian noise,” J. Opt. Soc. Am. A 25, 919–929 (2008).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).

[CrossRef]

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

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).

[CrossRef]

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).

[CrossRef]

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

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

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

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

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

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

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

[CrossRef]

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).

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

[CrossRef]

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).

[CrossRef]

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

[CrossRef]

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

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).

[CrossRef]

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).

[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

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

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).

[CrossRef]

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

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

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

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

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

[CrossRef]

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for pathological analysis of skin,” Appl. Opt. 48, D236–D246 (2009).

[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).

[CrossRef]

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

Y. Zhao, J. Yang, Q. Zhang, Q. Pan, and Y. Cheng., “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. (2011).

[CrossRef]

M. Choi, R. Kim, M. Nam, and O. Hong, “Fusion of multispectral and panchromatic satellite images using the curvelet transform,” IEEE Geosci. Remote Sens. Lett. 2, 136–140 (2005).

[CrossRef]

W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process. 20, 1838–1857 (2011).

[CrossRef]

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

[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).

[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).

[CrossRef]

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

[CrossRef]

F. Nencini, A. Garzelli, S. Baronti, and L. Alparone, “Remote sensing image fusion using the curvelet transform,” Information Fusion 8, 143–156 (2007).

[CrossRef]

S. L. Jacques, J. C. Ramella-Roman, and K. Lee, “Imaging skin pathology with polarized light,” J. Biomed. Opt. 7, 329–340 (2002).

[CrossRef]

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

R. S. Gurjar, V. Backman, L. T. Perelman, I. Georgakoudi, K. Badizadegan, I. Itzkan, R. R. Dasari, and M. S. Feld, “Imaging human epithelial properties with polarized lightscattering spectroscopy,” Nat. Med. 7, 1245–1248 (2001).

[CrossRef]

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature 381, 607–609 (1996).

[CrossRef]

Y. Zhao, Q. Pan, and H. Zhang, “New polarization imaging method based on spatially adaptive wavelet image fusion,” Opt. Eng. 45, 123202 (2006).

[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proc. SPIE 6812, 681207 (2008).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A non local algorithm for image denoising,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Y. Zhao, L. Zhang, and Q. Pan, “Spectropolarimetric imaging for anomaly epithelial tissue detection,” in Sequence and Genome Analysis: Methods and Applications (CreateSpace, 2011), pp. 297–330.

S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).

L. Capobianco, A. Garzelli, F. Nencini, L. Alparone, and S. Baronti, “Spatial enhancement of hyperion hyperspectral data through ALI panchromatic image,” in IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2007), pp. 5158–5161.

T. Chan, S. Esedoglu, F. Park, and A. Yip, “Recent development in total variation image restoration,” in Mathematical Models of Computer VisionN. Paragios, Y. Chen, and O. Faugeras, eds. (Springer Verlag, 2005), pp. 17–30.

H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Conference on Neural Information Processing Systems (MIT Press, 2007), pp. 801–808.