L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed poisson-gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011).

[CrossRef]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

T. Goldstein and S. Osher, “The split bregman method for l1 regularized problems,” SIAM J. Imag. Sci.2, 323–343 (2009).

[CrossRef]

S. Babacan, R. Molina, and A. Katsaggelos, “Parameter estimation in tv image restoration using variational distribution approximation,” IEEE Trans. Image Process.17, 326–339 (2008).

[CrossRef]
[PubMed]

B. Zhang, J. Fadili, and J. Starck, “Wavelets, ridgelets, and curvelets for poisson noise removal,” IEEE Trans. Image Process.17, 1093–1108 (2008).

[CrossRef]
[PubMed]

T. Le, R. Chartrand, and T. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vision27, 257–263 (2007).

[CrossRef]

G. Gilboa, N. Sochen, and Y. Zeevi, “Estimation of optimal pde-based denoising in the snr sense,” IEEE Trans. Image Process.15, 2269–2280 (2006).

[CrossRef]
[PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct,” J. X-Ray Sci. Technol.14, 119–139 (2006).

P. La Rivière and D. Billmire, “Reduction of noise-induced streak artifacts in x-ray computed tomography through spline-based penalized-likelihood sinogram smoothing,” IEEE Trans. Med. Imaging24, 105–111 (2005).

[CrossRef]
[PubMed]

P. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose ct,” Med. Phys.32, 1676 (2005).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision20, 89–97 (2004).

[CrossRef]

T. Chan and P. Mulet, “On the convergence of the lagged diffusivity fixed point method in total variation image restoration,” SIAM J. Numer. Anal.36, 354–367 (1999).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D60, 259–268 (1992).

[CrossRef]

K. Lange, R. Carson, and , “Em reconstruction algorithms for emission and transmission tomography.” J. Comput. Assist. Tomogr.8, 306 (1984).

[PubMed]

T. Le, R. Chartrand, and T. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vision27, 257–263 (2007).

[CrossRef]

S. Babacan, R. Molina, and A. Katsaggelos, “Parameter estimation in tv image restoration using variational distribution approximation,” IEEE Trans. Image Process.17, 326–339 (2008).

[CrossRef]
[PubMed]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

P. La Rivière and D. Billmire, “Reduction of noise-induced streak artifacts in x-ray computed tomography through spline-based penalized-likelihood sinogram smoothing,” IEEE Trans. Med. Imaging24, 105–111 (2005).

[CrossRef]
[PubMed]

M. Unger, T. Pock, and H. Bischof, “Continuous globally optimal image segmentation with local constraints,” in Computer Vision Winter Workshop at Slovenian Pattern Recognition Society, Ljubljana, Slovenia (2008).

F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed poisson-gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011).

[CrossRef]

H. Gach, C. Tanase, and F. Boada, “2d & 3d shepp-logan phantom standards for mri,” in IEEE 19th International Conference on Systems Engineering 2008 (IEEE, 2008), pp. 521–526.

[CrossRef]

K. Lange, R. Carson, and , “Em reconstruction algorithms for emission and transmission tomography.” J. Comput. Assist. Tomogr.8, 306 (1984).

[PubMed]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision20, 89–97 (2004).

[CrossRef]

T. Chan and P. Mulet, “On the convergence of the lagged diffusivity fixed point method in total variation image restoration,” SIAM J. Numer. Anal.36, 354–367 (1999).

[CrossRef]

T. Le, R. Chartrand, and T. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vision27, 257–263 (2007).

[CrossRef]

B. Zhang, J. Fadili, and J. Starck, “Wavelets, ridgelets, and curvelets for poisson noise removal,” IEEE Trans. Image Process.17, 1093–1108 (2008).

[CrossRef]
[PubMed]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D60, 259–268 (1992).

[CrossRef]

H. Gach, C. Tanase, and F. Boada, “2d & 3d shepp-logan phantom standards for mri,” in IEEE 19th International Conference on Systems Engineering 2008 (IEEE, 2008), pp. 521–526.

[CrossRef]

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

G. Gilboa, N. Sochen, and Y. Zeevi, “Estimation of optimal pde-based denoising in the snr sense,” IEEE Trans. Image Process.15, 2269–2280 (2006).

[CrossRef]
[PubMed]

T. Goldstein and S. Osher, “The split bregman method for l1 regularized problems,” SIAM J. Imag. Sci.2, 323–343 (2009).

[CrossRef]

H. Lu, T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose ct projections and noise treatment by scale transformations,” in in the IEEE Nuclear Science Symposium Conference 2001 Record (IEEE, 2001), Vol. 3, pp. 1662–1666.

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

J. Hsieh, Computed tomography: principles, design, artifacts, and recent advances (Society of Photo Optical, 2003), Vol. 114.

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Service Center, 1988), p. 327.

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct,” J. X-Ray Sci. Technol.14, 119–139 (2006).

S. Babacan, R. Molina, and A. Katsaggelos, “Parameter estimation in tv image restoration using variational distribution approximation,” IEEE Trans. Image Process.17, 326–339 (2008).

[CrossRef]
[PubMed]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

P. La Rivière and D. Billmire, “Reduction of noise-induced streak artifacts in x-ray computed tomography through spline-based penalized-likelihood sinogram smoothing,” IEEE Trans. Med. Imaging24, 105–111 (2005).

[CrossRef]
[PubMed]

P. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose ct,” Med. Phys.32, 1676 (2005).

[CrossRef]
[PubMed]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

K. Lange, R. Carson, and , “Em reconstruction algorithms for emission and transmission tomography.” J. Comput. Assist. Tomogr.8, 306 (1984).

[PubMed]

T. Le, R. Chartrand, and T. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vision27, 257–263 (2007).

[CrossRef]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

H. Lu, T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose ct projections and noise treatment by scale transformations,” in in the IEEE Nuclear Science Symposium Conference 2001 Record (IEEE, 2001), Vol. 3, pp. 1662–1666.

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

H. Lu, T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose ct projections and noise treatment by scale transformations,” in in the IEEE Nuclear Science Symposium Conference 2001 Record (IEEE, 2001), Vol. 3, pp. 1662–1666.

B. Wohlberg and Y. Lin, “Upre method for total variation parameter selection,” Tech. Report, Los Alamos National Laboratory (LANL) (2008).

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

H. Lu, T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose ct projections and noise treatment by scale transformations,” in in the IEEE Nuclear Science Symposium Conference 2001 Record (IEEE, 2001), Vol. 3, pp. 1662–1666.

F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed poisson-gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011).

[CrossRef]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

S. Babacan, R. Molina, and A. Katsaggelos, “Parameter estimation in tv image restoration using variational distribution approximation,” IEEE Trans. Image Process.17, 326–339 (2008).

[CrossRef]
[PubMed]

M. Tabuchi, N. Yamane, and Y. Morikawa, “Adaptive wiener filter based on gaussian mixture model for denoising chest x-ray ct image,” in IEEE Proceedings of SICE 2007 Annual Conference (IEEE, 2007), pp. 682–689.

[CrossRef]

T. Chan and P. Mulet, “On the convergence of the lagged diffusivity fixed point method in total variation image restoration,” SIAM J. Numer. Anal.36, 354–367 (1999).

[CrossRef]

T. Goldstein and S. Osher, “The split bregman method for l1 regularized problems,” SIAM J. Imag. Sci.2, 323–343 (2009).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D60, 259–268 (1992).

[CrossRef]

L. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” in Proceedings of the IEEE International Conference on Image Processing1994 (IEEE, 1994), vol. 1, pp. 31–35.

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct,” J. X-Ray Sci. Technol.14, 119–139 (2006).

M. Unger, T. Pock, and H. Bischof, “Continuous globally optimal image segmentation with local constraints,” in Computer Vision Winter Workshop at Slovenian Pattern Recognition Society, Ljubljana, Slovenia (2008).

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D60, 259–268 (1992).

[CrossRef]

L. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” in Proceedings of the IEEE International Conference on Image Processing1994 (IEEE, 1994), vol. 1, pp. 31–35.

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct,” J. X-Ray Sci. Technol.14, 119–139 (2006).

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Service Center, 1988), p. 327.

G. Gilboa, N. Sochen, and Y. Zeevi, “Estimation of optimal pde-based denoising in the snr sense,” IEEE Trans. Image Process.15, 2269–2280 (2006).

[CrossRef]
[PubMed]

B. Zhang, J. Fadili, and J. Starck, “Wavelets, ridgelets, and curvelets for poisson noise removal,” IEEE Trans. Image Process.17, 1093–1108 (2008).

[CrossRef]
[PubMed]

M. Tabuchi, N. Yamane, and Y. Morikawa, “Adaptive wiener filter based on gaussian mixture model for denoising chest x-ray ct image,” in IEEE Proceedings of SICE 2007 Annual Conference (IEEE, 2007), pp. 682–689.

[CrossRef]

H. Gach, C. Tanase, and F. Boada, “2d & 3d shepp-logan phantom standards for mri,” in IEEE 19th International Conference on Systems Engineering 2008 (IEEE, 2008), pp. 521–526.

[CrossRef]

M. Unger, T. Pock, and H. Bischof, “Continuous globally optimal image segmentation with local constraints,” in Computer Vision Winter Workshop at Slovenian Pattern Recognition Society, Ljubljana, Slovenia (2008).

F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed poisson-gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011).

[CrossRef]

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

B. Wohlberg and Y. Lin, “Upre method for total variation parameter selection,” Tech. Report, Los Alamos National Laboratory (LANL) (2008).

M. Tabuchi, N. Yamane, and Y. Morikawa, “Adaptive wiener filter based on gaussian mixture model for denoising chest x-ray ct image,” in IEEE Proceedings of SICE 2007 Annual Conference (IEEE, 2007), pp. 682–689.

[CrossRef]

G. Gilboa, N. Sochen, and Y. Zeevi, “Estimation of optimal pde-based denoising in the snr sense,” IEEE Trans. Image Process.15, 2269–2280 (2006).

[CrossRef]
[PubMed]

B. Zhang, J. Fadili, and J. Starck, “Wavelets, ridgelets, and curvelets for poisson noise removal,” IEEE Trans. Image Process.17, 1093–1108 (2008).

[CrossRef]
[PubMed]

L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

A. Berrington de Gonzalez, M. Mahesh, K. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the united states in 2007,” Arch. Intern Med.169, 2071 (2009).

[CrossRef]
[PubMed]

F. Luisier, T. Blu, and M. Unser, “Image denoising in mixed poisson-gaussian noise,” IEEE Trans. Image Process.20(3), 696–708 (2011).

[CrossRef]

B. Zhang, J. Fadili, and J. Starck, “Wavelets, ridgelets, and curvelets for poisson noise removal,” IEEE Trans. Image Process.17, 1093–1108 (2008).

[CrossRef]
[PubMed]

G. Gilboa, N. Sochen, and Y. Zeevi, “Estimation of optimal pde-based denoising in the snr sense,” IEEE Trans. Image Process.15, 2269–2280 (2006).

[CrossRef]
[PubMed]

S. Babacan, R. Molina, and A. Katsaggelos, “Parameter estimation in tv image restoration using variational distribution approximation,” IEEE Trans. Image Process.17, 326–339 (2008).

[CrossRef]
[PubMed]

A. Schilham, B. van Ginneken, H. Gietema, and M. Prokop, “Local noise weighted filtering for emphysema scoring of low-dose ct images,” IEEE Trans. Med. Imaging25, 451–463 (2006).

[CrossRef]
[PubMed]

P. La Rivière and D. Billmire, “Reduction of noise-induced streak artifacts in x-ray computed tomography through spline-based penalized-likelihood sinogram smoothing,” IEEE Trans. Med. Imaging24, 105–111 (2005).

[CrossRef]
[PubMed]

J. Wang, T. Li, H. Lu, and Z. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE Trans. Med. Imaging25, 1272–1283 (2006).

[CrossRef]
[PubMed]

T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose x-ray ct,” IEEE Trans. Nucl. Sci.51, 2505–2513 (2004).

[CrossRef]

K. Lange, R. Carson, and , “Em reconstruction algorithms for emission and transmission tomography.” J. Comput. Assist. Tomogr.8, 306 (1984).

[PubMed]

T. Le, R. Chartrand, and T. Asaki, “A variational approach to reconstructing images corrupted by poisson noise,” J. Math. Imaging Vision27, 257–263 (2007).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision20, 89–97 (2004).

[CrossRef]

E. Sidky, C. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam ct,” J. X-Ray Sci. Technol.14, 119–139 (2006).

P. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose ct,” Med. Phys.32, 1676 (2005).

[CrossRef]
[PubMed]

L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Computer analysis of images and patterns,” Pattern Recogn.44, 1990–1998 (2011).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D60, 259–268 (1992).

[CrossRef]

T. Goldstein and S. Osher, “The split bregman method for l1 regularized problems,” SIAM J. Imag. Sci.2, 323–343 (2009).

[CrossRef]

T. Chan and P. Mulet, “On the convergence of the lagged diffusivity fixed point method in total variation image restoration,” SIAM J. Numer. Anal.36, 354–367 (1999).

[CrossRef]

M. Unger, T. Pock, and H. Bischof, “Continuous globally optimal image segmentation with local constraints,” in Computer Vision Winter Workshop at Slovenian Pattern Recognition Society, Ljubljana, Slovenia (2008).

A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Service Center, 1988), p. 327.

H. Lu, T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose ct projections and noise treatment by scale transformations,” in in the IEEE Nuclear Science Symposium Conference 2001 Record (IEEE, 2001), Vol. 3, pp. 1662–1666.

J. Hsieh, Computed tomography: principles, design, artifacts, and recent advances (Society of Photo Optical, 2003), Vol. 114.

M. Tabuchi, N. Yamane, and Y. Morikawa, “Adaptive wiener filter based on gaussian mixture model for denoising chest x-ray ct image,” in IEEE Proceedings of SICE 2007 Annual Conference (IEEE, 2007), pp. 682–689.

[CrossRef]

L. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” in Proceedings of the IEEE International Conference on Image Processing1994 (IEEE, 1994), vol. 1, pp. 31–35.

B. Wohlberg and Y. Lin, “Upre method for total variation parameter selection,” Tech. Report, Los Alamos National Laboratory (LANL) (2008).

H. Gach, C. Tanase, and F. Boada, “2d & 3d shepp-logan phantom standards for mri,” in IEEE 19th International Conference on Systems Engineering 2008 (IEEE, 2008), pp. 521–526.

[CrossRef]