D. Q. Chen and L. Z. Cheng, “Spatially adapted total variation model to remove multiplicative noise,” IEEE Trans. Image Process. 21, 1650–1662 (2012).

[CrossRef]

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. Image Process. 21, 983–995 (2012).

[CrossRef]

S. Yun and H. Woo, “A new multiplicative denoising variational model based on m-th root transformation,” IEEE Trans. Image Process. 21, 2523–2533 (2012).

[CrossRef]

Y. Q. Dong, M. Hintermüller, and M. M. Rincon-Camacho, “Automated regularization parameter selection in multi-scale variation models for image restoration,” J. Math. Imaging Vision 40, 82–104 (2011).

[CrossRef]

J. Bioucas-Dias and M. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).

[CrossRef]

G. Steidl and T. Teuber, “Removing multiplicative noise by Douglas–Rachford splitting methods,” J. Math. Imag. Vis. 36, 168–184 (2010).

[CrossRef]

A. Wong, A. Mishra, K. Bizheva, and D. A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery,” Opt. Express 18, 8338–8352 (2010).

[CrossRef]

K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM J. Imag. Sci. 3, 492–526 (2010).

[CrossRef]

C. L. Wu and X. C. Tai, “Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models,” SIAM J. Imag. Sci. 3, 300–339 (2010).

[CrossRef]

F. Li, M. Ng, and C. Shen, “Multiplicative noise removal with spatial-varying regularization parameters,” SIAM J. Imag. Sci. 3, 1–20 (2010).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[CrossRef]

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

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

[CrossRef]

H. Z. Chen, J. P. Song, and X. C. Tai, “A dual algorithm for minimization of the LLT model,” Adv. Comput. Math. 31, 115–130 (2009).

[CrossRef]

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

G. Aubert and J. F. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).

[CrossRef]

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imag. Sci. 1, 294–321 (2008).

[CrossRef]

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

M. Lysaker and X. C. Tai, “Iterative image restoration combining total variation minimization and a second-order functional,” Int. J. Comput. Vis. 66, 5–18 (2006).

[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Variational denoising of partly textured images by spatially varying constraints,” IEEE Trans. Image Process. 15, 2281–2289 (2006).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model Simul. 4, 490–530 (2005).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vis. 20, 89–97 (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]

M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Trans. Image Process. 121579–1590 (2003).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

T. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM J. Sci. Comput. 22, 503–516 (2000).

[CrossRef]

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

[CrossRef]

P. J. Green, “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika 82, 711–732 (1995).

[CrossRef]

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

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

G. Aubert and J. F. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).

[CrossRef]

G. Aubert and J. F. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).

[CrossRef]

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[CrossRef]

J. Bioucas-Dias and M. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[CrossRef]

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. Image Process. 21, 983–995 (2012).

[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]

K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM J. Imag. Sci. 3, 492–526 (2010).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model Simul. 4, 490–530 (2005).

[CrossRef]

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vis. 20, 89–97 (2004).

[CrossRef]

T. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM J. Sci. Comput. 22, 503–516 (2000).

[CrossRef]

D. Q. Chen and L. Z. Cheng, “Spatially adapted total variation model to remove multiplicative noise,” IEEE Trans. Image Process. 21, 1650–1662 (2012).

[CrossRef]

H. Z. Chen, J. P. Song, and X. C. Tai, “A dual algorithm for minimization of the LLT model,” Adv. Comput. Math. 31, 115–130 (2009).

[CrossRef]

D. Q. Chen and L. Z. Cheng, “Spatially adapted total variation model to remove multiplicative noise,” IEEE Trans. Image Process. 21, 1650–1662 (2012).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model Simul. 4, 490–530 (2005).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[CrossRef]

Y. Q. Dong, M. Hintermüller, and M. M. Rincon-Camacho, “Automated regularization parameter selection in multi-scale variation models for image restoration,” J. Math. Imaging Vision 40, 82–104 (2011).

[CrossRef]

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

[CrossRef]

E. Esser, “Applications of Lagrangian-based alternating direction methods and connections to split Bregman,” (UCLA, 2009).

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

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

J. Bioucas-Dias and M. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).

[CrossRef]

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Variational denoising of partly textured images by spatially varying constraints,” IEEE Trans. Image Process. 15, 2281–2289 (2006).

[CrossRef]

T. Goldstein, B. O’Donoghue, and S. Setzer, “Fast alternating direction optimization methods,” , (UCLA, 2012).

P. J. Green, “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika 82, 711–732 (1995).

[CrossRef]

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

Y. Q. Dong, M. Hintermüller, and M. M. Rincon-Camacho, “Automated regularization parameter selection in multi-scale variation models for image restoration,” J. Math. Imaging Vision 40, 82–104 (2011).

[CrossRef]

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

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

[CrossRef]

K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM J. Imag. Sci. 3, 492–526 (2010).

[CrossRef]

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. Image Process. 21, 983–995 (2012).

[CrossRef]

F. Li, M. Ng, and C. Shen, “Multiplicative noise removal with spatial-varying regularization parameters,” SIAM J. Imag. Sci. 3, 1–20 (2010).

[CrossRef]

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

L. Rudin, P. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Sets in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Trans. Image Process. 121579–1590 (2003).

[CrossRef]

M. Lysaker and X. C. Tai, “Iterative image restoration combining total variation minimization and a second-order functional,” Int. J. Comput. Vis. 66, 5–18 (2006).

[CrossRef]

M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Trans. Image Process. 121579–1590 (2003).

[CrossRef]

T. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM J. Sci. Comput. 22, 503–516 (2000).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model Simul. 4, 490–530 (2005).

[CrossRef]

T. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM J. Sci. Comput. 22, 503–516 (2000).

[CrossRef]

F. Li, M. Ng, and C. Shen, “Multiplicative noise removal with spatial-varying regularization parameters,” SIAM J. Imag. Sci. 3, 1–20 (2010).

[CrossRef]

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

[CrossRef]

T. Goldstein, B. O’Donoghue, and S. Setzer, “Fast alternating direction optimization methods,” , (UCLA, 2012).

C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images (SciTech Publishing, Inc., 2004).

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imag. Sci. 1, 294–321 (2008).

[CrossRef]

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

L. Rudin, P. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Sets in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.

K. Papafitsoros and C. B. Schönlieb, “A combined first and second order variational approach for image reconstruction,” J. Math. Imaging Vis. (2013). doi 10.1007/s10851-013-0445-4.

[CrossRef]

K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM J. Imag. Sci. 3, 492–526 (2010).

[CrossRef]

C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images (SciTech Publishing, Inc., 2004).

Y. Q. Dong, M. Hintermüller, and M. M. Rincon-Camacho, “Automated regularization parameter selection in multi-scale variation models for image restoration,” J. Math. Imaging Vision 40, 82–104 (2011).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

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

L. Rudin, P. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Sets in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.

K. Papafitsoros and C. B. Schönlieb, “A combined first and second order variational approach for image reconstruction,” J. Math. Imaging Vis. (2013). doi 10.1007/s10851-013-0445-4.

[CrossRef]

T. Goldstein, B. O’Donoghue, and S. Setzer, “Fast alternating direction optimization methods,” , (UCLA, 2012).

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]

F. Li, M. Ng, and C. Shen, “Multiplicative noise removal with spatial-varying regularization parameters,” SIAM J. Imag. Sci. 3, 1–20 (2010).

[CrossRef]

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imag. Sci. 1, 294–321 (2008).

[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]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Variational denoising of partly textured images by spatially varying constraints,” IEEE Trans. Image Process. 15, 2281–2289 (2006).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

H. Z. Chen, J. P. Song, and X. C. Tai, “A dual algorithm for minimization of the LLT model,” Adv. Comput. Math. 31, 115–130 (2009).

[CrossRef]

G. Steidl and T. Teuber, “Removing multiplicative noise by Douglas–Rachford splitting methods,” J. Math. Imag. Vis. 36, 168–184 (2010).

[CrossRef]

C. L. Wu and X. C. Tai, “Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models,” SIAM J. Imag. Sci. 3, 300–339 (2010).

[CrossRef]

H. Z. Chen, J. P. Song, and X. C. Tai, “A dual algorithm for minimization of the LLT model,” Adv. Comput. Math. 31, 115–130 (2009).

[CrossRef]

M. Lysaker and X. C. Tai, “Iterative image restoration combining total variation minimization and a second-order functional,” Int. J. Comput. Vis. 66, 5–18 (2006).

[CrossRef]

M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Trans. Image Process. 121579–1590 (2003).

[CrossRef]

G. Steidl and T. Teuber, “Removing multiplicative noise by Douglas–Rachford splitting methods,” J. Math. Imag. Vis. 36, 168–184 (2010).

[CrossRef]

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. Image Process. 21, 983–995 (2012).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[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]

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

[CrossRef]

S. Yun and H. Woo, “A new multiplicative denoising variational model based on m-th root transformation,” IEEE Trans. Image Process. 21, 2523–2533 (2012).

[CrossRef]

C. L. Wu and X. C. Tai, “Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models,” SIAM J. Imag. Sci. 3, 300–339 (2010).

[CrossRef]

S. Yun and H. Woo, “A new multiplicative denoising variational model based on m-th root transformation,” IEEE Trans. Image Process. 21, 2523–2533 (2012).

[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Variational denoising of partly textured images by spatially varying constraints,” IEEE Trans. Image Process. 15, 2281–2289 (2006).

[CrossRef]

H. Z. Chen, J. P. Song, and X. C. Tai, “A dual algorithm for minimization of the LLT model,” Adv. Comput. Math. 31, 115–130 (2009).

[CrossRef]

P. J. Green, “Reversible jump Markov chain Monte Carlo computation and Bayesian model determination,” Biometrika 82, 711–732 (1995).

[CrossRef]

S. Yun and H. Woo, “A new multiplicative denoising variational model based on m-th root transformation,” IEEE Trans. Image Process. 21, 2523–2533 (2012).

[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]

M. Lysaker, A. Lundervold, and X. C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Trans. Image Process. 121579–1590 (2003).

[CrossRef]

S. Lefkimmiatis, A. Bourquard, and M. Unser, “Hessian-based norm regularization for image restoration with biomedical applications,” IEEE Trans. Image Process. 21, 983–995 (2012).

[CrossRef]

G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Variational denoising of partly textured images by spatially varying constraints,” IEEE Trans. Image Process. 15, 2281–2289 (2006).

[CrossRef]

D. Q. Chen and L. Z. Cheng, “Spatially adapted total variation model to remove multiplicative noise,” IEEE Trans. Image Process. 21, 1650–1662 (2012).

[CrossRef]

J. Bioucas-Dias and M. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).

[CrossRef]

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

[CrossRef]

M. Lysaker and X. C. Tai, “Iterative image restoration combining total variation minimization and a second-order functional,” Int. J. Comput. Vis. 66, 5–18 (2006).

[CrossRef]

M. Bertero, P. Boccacci, G. Desiderà, and G. Vicidomini, “Image deblurring with Poisson data: from cells to galaxies,” Inverse Problems 25, 123006 (2009).

[CrossRef]

G. Steidl and T. Teuber, “Removing multiplicative noise by Douglas–Rachford splitting methods,” J. Math. Imag. Vis. 36, 168–184 (2010).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vis. 20, 89–97 (2004).

[CrossRef]

Y. Q. Dong, M. Hintermüller, and M. M. Rincon-Camacho, “Automated regularization parameter selection in multi-scale variation models for image restoration,” J. Math. Imaging Vision 40, 82–104 (2011).

[CrossRef]

M. Bertalmio, V. Caselles, B. Rougé, and A. Solé, “TV based image restoration with local constraints,” J. Sci. Comput. 19, 95–122 (2003).

[CrossRef]

A. Almansa, C. Ballester, V. Caselles, and G. Haro, “A TV based restoration model with local constraints,” J. Sci. Comput. 34, 209–236 (2008).

[CrossRef]

F. Li, C. M. Shen, J. S. Fan, and C. L. Shen, “Image restoration combining a total variational filter and a fourth-order filter,” J. Visual Commun. Image Rep. 18, 322–330 (2007).

[CrossRef]

A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model Simul. 4, 490–530 (2005).

[CrossRef]

S. Q. Huang, D. Z. Liu, G. Q. Gao, and X. J. Guo, “A novel method for speckle noise reduction and ship target detection in SAR images,” Pattern Recogn. 42, 1533–1542 (2009).

[CrossRef]

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

G. Aubert and J. F. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).

[CrossRef]

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imag. Sci. 1, 294–321 (2008).

[CrossRef]

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

[CrossRef]

F. Li, M. Ng, and C. Shen, “Multiplicative noise removal with spatial-varying regularization parameters,” SIAM J. Imag. Sci. 3, 1–20 (2010).

[CrossRef]

K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM J. Imag. Sci. 3, 492–526 (2010).

[CrossRef]

C. L. Wu and X. C. Tai, “Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models,” SIAM J. Imag. Sci. 3, 300–339 (2010).

[CrossRef]

T. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM J. Sci. Comput. 22, 503–516 (2000).

[CrossRef]

L. Rudin, P. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Sets in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.

E. Esser, “Applications of Lagrangian-based alternating direction methods and connections to split Bregman,” (UCLA, 2009).

T. Goldstein, B. O’Donoghue, and S. Setzer, “Fast alternating direction optimization methods,” , (UCLA, 2012).

K. Papafitsoros and C. B. Schönlieb, “A combined first and second order variational approach for image reconstruction,” J. Math. Imaging Vis. (2013). doi 10.1007/s10851-013-0445-4.

[CrossRef]

J. Goodman, Speckle Phenomena in Optics: Theory and Applications (Roberts & Company, 2007).

C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images (SciTech Publishing, Inc., 2004).