B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011).

[CrossRef]

J. M. Bardsley, and J. Goldes, “An iterative method for edge-preserving MAP estimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171–185 (2010).

[CrossRef]

V. B. S. Prasath and A. Singh, “A hybrid convex variational model for image restoration,” Appl. Math. Comput. 215, 3655–3664 (2010).

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regularization in image restoration,” J. Comput. Appl. Math. 225, 478–486 (2009).

[CrossRef]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007).

[CrossRef]

D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image denoising,” J. Comput. Appl. Math. 210, 222–231 (2007).

[CrossRef]

R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006).

[CrossRef]

P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133–144 (2005).

[CrossRef]

R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002).

[CrossRef]

M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Comput. Vis. Image Underst. 88, 76–93 (2002).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography,” IEEE Trans. Image Process. 7, 204–221 (1998).

[CrossRef]

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

L. Blanc-Feraud, and M. Barlaud, “Edge preserving restoration of astrophysical images,” Vistas Astron. 40, 531–538 (1996).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

J. M. Bardsley, and J. Goldes, “An iterative method for edge-preserving MAP estimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171–185 (2010).

[CrossRef]

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

L. Blanc-Feraud, and M. Barlaud, “Edge preserving restoration of astrophysical images,” Vistas Astron. 40, 531–538 (1996).

[CrossRef]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

L. Blanc-Feraud, and M. Barlaud, “Edge preserving restoration of astrophysical images,” Vistas Astron. 40, 531–538 (1996).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004).

[CrossRef]

A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography,” IEEE Trans. Image Process. 7, 204–221 (1998).

[CrossRef]

R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004).

[CrossRef]

P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133–144 (2005).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

L. Y. Chen, M.-C. Pan, and M.-C. Pan, “Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography,” Appl. Opt. 51, 43–54 (2012).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography,” IEEE Trans. Image Process. 7, 204–221 (1998).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

D. F. Yu, and J. A. Fessler, “Three-dimensional non-local edge-preserving regularization for PET transmission reconstruction,” in Nuclear Science Symposium Conference Record (IEEE, 2000), pp. 1566–1570.

X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regularization in image restoration,” J. Comput. Appl. Math. 225, 478–486 (2009).

[CrossRef]

J. M. Bardsley, and J. Goldes, “An iterative method for edge-preserving MAP estimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171–185 (2010).

[CrossRef]

B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regularization in image restoration,” J. Comput. Appl. Math. 225, 478–486 (2009).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002).

[CrossRef]

B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011).

[CrossRef]

D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image denoising,” J. Comput. Appl. Math. 210, 222–231 (2007).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Comput. Vis. Image Underst. 88, 76–93 (2002).

[CrossRef]

D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image denoising,” J. Comput. Appl. Math. 210, 222–231 (2007).

[CrossRef]

B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011).

[CrossRef]

L. Y. Chen, M.-C. Pan, and M.-C. Pan, “Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography,” Appl. Opt. 51, 43–54 (2012).

[CrossRef]

L. Y. Chen, M.-C. Pan, and M.-C. Pan, “Implementation of edge-preserving regularization for frequency-domain diffuse optical tomography,” Appl. Opt. 51, 43–54 (2012).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

V. B. S. Prasath and A. Singh, “A hybrid convex variational model for image restoration,” Appl. Math. Comput. 215, 3655–3664 (2010).

P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133–144 (2005).

[CrossRef]

R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006).

[CrossRef]

M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Comput. Vis. Image Underst. 88, 76–93 (2002).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004).

[CrossRef]

V. B. S. Prasath and A. Singh, “A hybrid convex variational model for image restoration,” Appl. Math. Comput. 215, 3655–3664 (2010).

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007).

[CrossRef]

D. F. Yu, and J. A. Fessler, “Three-dimensional non-local edge-preserving regularization for PET transmission reconstruction,” in Nuclear Science Symposium Conference Record (IEEE, 2000), pp. 1566–1570.

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007).

[CrossRef]

V. B. S. Prasath and A. Singh, “A hybrid convex variational model for image restoration,” Appl. Math. Comput. 215, 3655–3664 (2010).

M. Rivera and J. L. Marroquin, “Adaptive rest condition potentials: first and second order edge-preserving regularization,” Comput. Vis. Image Underst. 88, 76–93 (2002).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “A non-linear regularized constrained impedance inversion,” Geophys. Prospect. 55, 819–833 (2007).

[CrossRef]

B. Omrane, Y. Goussard, and J. Laurin, “Constrained inverse near-field scattering using high resolution wire grid models,” IEEE Trans. Antennas Propag. 59, 3710–3718 (2011).

[CrossRef]

P. Charbonnier, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Deterministic edge-preserving regularization in computed imaging,” IEEE Trans. Image Process. 6, 298–311 (1997).

[CrossRef]

A. H. Delaney and Y. Bresler, “Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography,” IEEE Trans. Image Process. 7, 204–221 (1998).

[CrossRef]

R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006).

[CrossRef]

S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998).

[CrossRef]

N. Villain, Y. Goussard, J. Idier, and M. Allain, “Three-dimensional edge-preserving image enhancement for computed tomography,” IEEE Trans. Med. Imag. 22, 1275–1287 (2003).

[CrossRef]

C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 460–472 (2000).

[CrossRef]

P. Lobel, L. Blanc-Feraud, Ch. Pichot, and M. Barlaud, “A new regularization scheme for inverse scattering,” Inverse Probl. 13, 403–410 (1997).

[CrossRef]

R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, “Efficient gradient projection methods for edge-preserving removal of Poisson noise,” Inverse Probl. 25, 045010 (2009).

[CrossRef]

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25, 123010 (2009).

[CrossRef]

H. Zhang, Z. Shang, and C. Yang, “Adaptive reconstruction method of impedance model with absolute and relative constraints,” J. Appl. Geophys. 67, 114–124 (2009).

[CrossRef]

M.-C. Pan, C. H. Chen, L. Y. Chen, M.-C. Pan, and Y. M. Shyr, “Highly resolved diffuse optical tomography: a systematic approach using high-pass filtering for value-preserved images,” J. Biomed. Opt. 13, 024022 (2008).

[CrossRef]

D. Lazzaro and L. B. Montefusco, “Edge-preserving wavelet thresholding for image denoising,” J. Comput. Appl. Math. 210, 222–231 (2007).

[CrossRef]

X. Gu and L. Gao, “A new method for parameter estimation of edge-preserving regularization in image restoration,” J. Comput. Appl. Math. 225, 478–486 (2009).

[CrossRef]

G. Vicidomini, P. Boccacci, A. Diaspro, and M. Bertero, “Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy,” J. Microsc. 234, 47–61 (2009).

[CrossRef]

P. J. Cassidy and G. K. Radda, “Molecular imaging perspectives,” J. R. Soc. Interface 2, 133–144 (2005).

[CrossRef]

A. Jalobeanu, L. Blance-Feraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002).

[CrossRef]

R. Casanova, A. Silva, and A. R. Borges, “MIT image reconstruction based on edge-preserving regularization,” Physiol. Meas. 25, 195–207 (2004).

[CrossRef]

J. M. Bardsley, and J. Goldes, “An iterative method for edge-preserving MAP estimation when data-noise is Poisson,” SIAM J. Sci. Comput. 32, 171–185 (2010).

[CrossRef]

L. Blanc-Feraud, and M. Barlaud, “Edge preserving restoration of astrophysical images,” Vistas Astron. 40, 531–538 (1996).

[CrossRef]

D. F. Yu, and J. A. Fessler, “Three-dimensional non-local edge-preserving regularization for PET transmission reconstruction,” in Nuclear Science Symposium Conference Record (IEEE, 2000), pp. 1566–1570.