D. Tzikas, A. Likas, and N. Galatsanos, “Variational Bayesian sparse kernel-based blind image deconvolution with student’s-t priors,” IEEE Trans. Image Process. 18, 753–764 (2009).

[CrossRef]
[PubMed]

Y. Huang, M. Ng, and Y. Wen, “A new total variation method for multiplicative noise removal,” SIAM J. Imaging Sci. 2, 20–40 (2009).

[CrossRef]

S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process. 18, 12–26 (2009).

[CrossRef]

C. L. Matson, K. Borelli, S. Jefferies, C. C. Beckner, Jr., E. K. Hege, and M. Lloyd-Hart, “Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects,” Appl. Opt. 48, A75–A92 (2009).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A fast total variation minimization method for image restoration,” Multiscale Model. Simul. 7, 774–795 (2008).

[CrossRef]

I. Kopriva, “Approach to blind image deconvolution by multiscale subband decomposition and independent component analysis,” J. Opt. Soc. Am. A 24, 973–983 (2007).

[CrossRef]

H. Zhu, Y. Lu, and Q. Wu, “Blind image deconvolution subject to bandwidth and total variation constraints,” Opt. Lett. 32, 2550–2552 (2007).

[CrossRef]

O. Haik and Y. Yitzhaky, “Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere,” Appl. Opt. 46, 8562–8572 (2007).

[CrossRef]
[PubMed]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. Image Process. 15, 3715–3727(2006).

[CrossRef]
[PubMed]

T. Chan and K. Chen, “An optimization-based multilevel algorithm for total variation image denoising,” Multiscale Model. Simul. 5, 615–645 (2006).

[CrossRef]

D. Krishnan, P. Lin, and X. Tai, “An efficient operator splitting method for noise removal in images,” Commun. Comput. Phys. 1, 847–858 (2006).

F. Sroubek and J. Flusser, “Multichannel blind deconvolution of spatially misaligned images,” IEEE Trans. Image Process. 14, 874–883 (2005).

[CrossRef]
[PubMed]

A. Rav-Acha and S. Peleg, “Two motion-blurred images are better than one,” Pattern Recogn. Lett. 26, 311–317 (2005).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 73 (2004).

[CrossRef]

F. Sroubek and J. Flusser, “Multichannel blind iterative image restoration,” IEEE Trans. Image Process. 12, 1094–1106 (2003).

[CrossRef]

G. Harikumar and Y. Bresler, “Exact image deconvolution from multiple FIR blurs,” IEEE Trans. Image Process. 8, 846–862 (1999).

[CrossRef]

G. Harikumar and Y. Bresler, “Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms,” IEEE Trans. Image Process. 8, 202–219 (1999).

[CrossRef]

T. Chan, G. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput. 20, 1964–1977 (1999).

[CrossRef]

T. Chan and C. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).

[CrossRef]
[PubMed]

C. Vogel and M. Oman, “Iterative method for total variation denoising,” SIAM J. Sci. Comput. 17, 227–238 (1996).

[CrossRef]

Y. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Process. 5, 416–428 (1996).

[CrossRef]
[PubMed]

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal. Process. Mag. 13, 43–64 (1996).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992).

[CrossRef]

A. Katsaggelos and K. Lay, “Maximum likelihood blur identification and image restoration using the EM algorithm,” IEEE Trans. Signal Process. 39, 729–733 (1991).

[CrossRef]

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

S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process. 18, 12–26 (2009).

[CrossRef]

G. Harikumar and Y. Bresler, “Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms,” IEEE Trans. Image Process. 8, 202–219 (1999).

[CrossRef]

G. Harikumar and Y. Bresler, “Exact image deconvolution from multiple FIR blurs,” IEEE Trans. Image Process. 8, 846–862 (1999).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 73 (2004).

[CrossRef]

T. Chan and K. Chen, “An optimization-based multilevel algorithm for total variation image denoising,” Multiscale Model. Simul. 5, 615–645 (2006).

[CrossRef]

T. Chan, G. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput. 20, 1964–1977 (1999).

[CrossRef]

T. Chan and C. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

T. Chan and K. Chen, “An optimization-based multilevel algorithm for total variation image denoising,” Multiscale Model. Simul. 5, 615–645 (2006).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992).

[CrossRef]

F. Sroubek and J. Flusser, “Multichannel blind deconvolution of spatially misaligned images,” IEEE Trans. Image Process. 14, 874–883 (2005).

[CrossRef]
[PubMed]

F. Sroubek and J. Flusser, “Multichannel blind iterative image restoration,” IEEE Trans. Image Process. 12, 1094–1106 (2003).

[CrossRef]

D. Tzikas, A. Likas, and N. Galatsanos, “Variational Bayesian sparse kernel-based blind image deconvolution with student’s-t priors,” IEEE Trans. Image Process. 18, 753–764 (2009).

[CrossRef]
[PubMed]

T. Chan, G. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput. 20, 1964–1977 (1999).

[CrossRef]

G. Harikumar and Y. Bresler, “Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms,” IEEE Trans. Image Process. 8, 202–219 (1999).

[CrossRef]

G. Harikumar and Y. Bresler, “Exact image deconvolution from multiple FIR blurs,” IEEE Trans. Image Process. 8, 846–862 (1999).

[CrossRef]

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal. Process. Mag. 13, 43–64 (1996).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A new total variation method for multiplicative noise removal,” SIAM J. Imaging Sci. 2, 20–40 (2009).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A fast total variation minimization method for image restoration,” Multiscale Model. Simul. 7, 774–795 (2008).

[CrossRef]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

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

A. Katsaggelos and K. Lay, “Maximum likelihood blur identification and image restoration using the EM algorithm,” IEEE Trans. Signal Process. 39, 729–733 (1991).

[CrossRef]

S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process. 18, 12–26 (2009).

[CrossRef]

R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. Image Process. 15, 3715–3727(2006).

[CrossRef]
[PubMed]

Y. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Process. 5, 416–428 (1996).

[CrossRef]
[PubMed]

D. Krishnan, P. Lin, and X. Tai, “An efficient operator splitting method for noise removal in images,” Commun. Comput. Phys. 1, 847–858 (2006).

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal. Process. Mag. 13, 43–64 (1996).

[CrossRef]

A. Katsaggelos and K. Lay, “Maximum likelihood blur identification and image restoration using the EM algorithm,” IEEE Trans. Signal Process. 39, 729–733 (1991).

[CrossRef]

D. Tzikas, A. Likas, and N. Galatsanos, “Variational Bayesian sparse kernel-based blind image deconvolution with student’s-t priors,” IEEE Trans. Image Process. 18, 753–764 (2009).

[CrossRef]
[PubMed]

D. Krishnan, P. Lin, and X. Tai, “An efficient operator splitting method for noise removal in images,” Commun. Comput. Phys. 1, 847–858 (2006).

R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. Image Process. 15, 3715–3727(2006).

[CrossRef]
[PubMed]

S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process. 18, 12–26 (2009).

[CrossRef]

R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. Image Process. 15, 3715–3727(2006).

[CrossRef]
[PubMed]

T. Chan, G. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput. 20, 1964–1977 (1999).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A new total variation method for multiplicative noise removal,” SIAM J. Imaging Sci. 2, 20–40 (2009).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A fast total variation minimization method for image restoration,” Multiscale Model. Simul. 7, 774–795 (2008).

[CrossRef]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

C. Vogel and M. Oman, “Iterative method for total variation denoising,” SIAM J. Sci. Comput. 17, 227–238 (1996).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992).

[CrossRef]

A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).

[CrossRef]
[PubMed]

A. Rav-Acha and S. Peleg, “Two motion-blurred images are better than one,” Pattern Recogn. Lett. 26, 311–317 (2005).

[CrossRef]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

A. Rav-Acha and S. Peleg, “Two motion-blurred images are better than one,” Pattern Recogn. Lett. 26, 311–317 (2005).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992).

[CrossRef]

A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).

[CrossRef]
[PubMed]

F. Sroubek and J. Flusser, “Multichannel blind deconvolution of spatially misaligned images,” IEEE Trans. Image Process. 14, 874–883 (2005).

[CrossRef]
[PubMed]

F. Sroubek and J. Flusser, “Multichannel blind iterative image restoration,” IEEE Trans. Image Process. 12, 1094–1106 (2003).

[CrossRef]

D. Krishnan, P. Lin, and X. Tai, “An efficient operator splitting method for noise removal in images,” Commun. Comput. Phys. 1, 847–858 (2006).

A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).

[CrossRef]
[PubMed]

A. M. Tekalp, Digital Video Processing (Prentice-Hall, 1995).

D. Tzikas, A. Likas, and N. Galatsanos, “Variational Bayesian sparse kernel-based blind image deconvolution with student’s-t priors,” IEEE Trans. Image Process. 18, 753–764 (2009).

[CrossRef]
[PubMed]

C. Vogel and M. Oman, “Iterative method for total variation denoising,” SIAM J. Sci. Comput. 17, 227–238 (1996).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A new total variation method for multiplicative noise removal,” SIAM J. Imaging Sci. 2, 20–40 (2009).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A fast total variation minimization method for image restoration,” Multiscale Model. Simul. 7, 774–795 (2008).

[CrossRef]

T. Chan and C. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

Y. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Process. 5, 416–428 (1996).

[CrossRef]
[PubMed]

O. Haik and Y. Yitzhaky, “Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere,” Appl. Opt. 46, 8562–8572 (2007).

[CrossRef]
[PubMed]

C. L. Matson, K. Borelli, S. Jefferies, C. C. Beckner, Jr., E. K. Hege, and M. Lloyd-Hart, “Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects,” Appl. Opt. 48, A75–A92 (2009).

[CrossRef]

D. Krishnan, P. Lin, and X. Tai, “An efficient operator splitting method for noise removal in images,” Commun. Comput. Phys. 1, 847–858 (2006).

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal. Process. Mag. 13, 43–64 (1996).

[CrossRef]

S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior,” IEEE Trans. Image Process. 18, 12–26 (2009).

[CrossRef]

T. Chan and C. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. Image Process. 15, 3715–3727(2006).

[CrossRef]
[PubMed]

F. Sroubek and J. Flusser, “Multichannel blind iterative image restoration,” IEEE Trans. Image Process. 12, 1094–1106 (2003).

[CrossRef]

F. Sroubek and J. Flusser, “Multichannel blind deconvolution of spatially misaligned images,” IEEE Trans. Image Process. 14, 874–883 (2005).

[CrossRef]
[PubMed]

Y. You and M. Kaveh, “A regularization approach to joint blur identification and image restoration,” IEEE Trans. Image Process. 5, 416–428 (1996).

[CrossRef]
[PubMed]

D. Tzikas, A. Likas, and N. Galatsanos, “Variational Bayesian sparse kernel-based blind image deconvolution with student’s-t priors,” IEEE Trans. Image Process. 18, 753–764 (2009).

[CrossRef]
[PubMed]

G. Harikumar and Y. Bresler, “Exact image deconvolution from multiple FIR blurs,” IEEE Trans. Image Process. 8, 846–862 (1999).

[CrossRef]

G. Harikumar and Y. Bresler, “Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms,” IEEE Trans. Image Process. 8, 202–219 (1999).

[CrossRef]

A. J. Patti, M. I. Sezan, and A. M. Tekalp, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Trans. Image Process. 6, 1064–1076 (1997).

[CrossRef]
[PubMed]

A. Katsaggelos and K. Lay, “Maximum likelihood blur identification and image restoration using the EM algorithm,” IEEE Trans. Signal Process. 39, 729–733 (1991).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 73 (2004).

[CrossRef]

M. Ng, L. Qi, Y. Yang, and Y. Huang, “On semismooth Newton’s methods for total variation minimization,” J. Math. Imaging Vision 27, 265–276 (2007).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A fast total variation minimization method for image restoration,” Multiscale Model. Simul. 7, 774–795 (2008).

[CrossRef]

T. Chan and K. Chen, “An optimization-based multilevel algorithm for total variation image denoising,” Multiscale Model. Simul. 5, 615–645 (2006).

[CrossRef]

A. Rav-Acha and S. Peleg, “Two motion-blurred images are better than one,” Pattern Recogn. Lett. 26, 311–317 (2005).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992).

[CrossRef]

Y. Huang, M. Ng, and Y. Wen, “A new total variation method for multiplicative noise removal,” SIAM J. Imaging Sci. 2, 20–40 (2009).

[CrossRef]

C. Vogel and M. Oman, “Iterative method for total variation denoising,” SIAM J. Sci. Comput. 17, 227–238 (1996).

[CrossRef]

T. Chan, G. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM J. Sci. Comput. 20, 1964–1977 (1999).

[CrossRef]

A. M. Tekalp, Digital Video Processing (Prentice-Hall, 1995).

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