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Edge-preserving reconstruction with contour-line smoothing and non-quadratic data-fidelity
1. | CREATIS (CNRS research unit UMR5220 and INSERM research unit U1044), INSA-Lyon, 69621 Villeurbanne Cedex, France, France |
2. | College of Life Science and Technology, Shanghai Jiao Tong University, Shanghai 200240, China |
References:
[1] |
E. Bae, J. Shi and X.-C. Tai, Graph cuts for curvature based image denoising, IEEE Trans. Image Process., 20 (2011), 1199-1210.
doi: 10.1109/TIP.2010.2090533. |
[2] |
M. Banham and A. Katsaggelos, Digital image restoration, IEEE Signal Processing Mag., 14 (1997), 24-41.
doi: 10.1109/79.581363. |
[3] |
L. Bedini, L. Benvenuti, E. Salerno and A. Tonazzini, A mixed-annealing algorithm for edge preserving image reconstruction using a limited number of projections, Signal Process., 32 (1993), 397-408.
doi: 10.1016/0165-1684(93)90009-Y. |
[4] |
M. Belge, M. Kilmer and E. Miller, Wavelet domain image restoration with adaptive edge-preserving regularization, IEEE Trans. Image Process., 9 (2000), 597-608.
doi: 10.1109/83.841937. |
[5] |
M. Belge, M. Kilmer and E. Miller, Efficient determination of multiple regularization parameters in a generalized L-curve framework, Inverse Problems, 18 (2002), 1161-1183.
doi: 10.1088/0266-5611/18/4/314. |
[6] |
L. Blanc-Féraud, S. Teboul, G. Aubert and M. Barlaud, Nonlinear regularization using constrained edges in image reconstruction, in Proc. IEEE Int. Conf. Image Processing, 2, Lausanne, Switzerland, (1996), 449-452.
doi: 10.1109/ICIP.1996.560882. |
[7] |
J.-F. Cai, R. Chan and M. Nikolova, Fast two-phase image deblurring under impulse noise, J. Math. Imaging Vis., 36 (2010), 46-53.
doi: 10.1007/s10851-009-0169-7. |
[8] |
R. Chan, Y. Dong and M. Hintermüller, An efficient two-phase $L^1$-TV method for restoring blurred images with impulse noise, IEEE Trans. Image Process., 19 (2010), 1731-1739.
doi: 10.1109/TIP.2010.2045148. |
[9] |
T. Chan, S. Kang and J. Shen, Euler's elastica and curvature-based inpainting, SIAM J. Appl. Math., 63 (2002), 564-592.
doi: 10.1137/S0036139901390088. |
[10] |
T. Chan, A. Marquina and P. Mulet, High-order total variation-based image restoration, SIAM J. Sci. Comput., 22 (2000), 503-516.
doi: 10.1137/S1064827598344169. |
[11] |
P. Charbonnier, L. Blanc-Féraud, G. Aubert and M. Barlaud, Deterministic edge-preserving regularization in computed imaging, IEEE Trans. Image Process., 6 (1997), 298-311.
doi: 10.1109/83.551699. |
[12] |
H. Choi and R. Baraniuk, Wavelet statistical models and Besov spaces, in Proc. SPIE, Wavelet Applications in Signal and Image Processing VII, vol. 3813, 1999, 489-501.
doi: 10.1007/978-0-387-21579-2_2. |
[13] |
E. Chouzenoux, J. Idier and S. Moussaoui, A majorize-minimize strategy for subspace optimization applied to image restoration, IEEE Trans. Image Process, 20 (2011), 1517-1528.
doi: 10.1109/TIP.2010.2103083. |
[14] |
A. Cohen, I. Daubechies and J.-C. Feauveau, Biorthogonal bases of compactly supported wavelets, Comm. Pure Appl. Math., 45 (1992), 485-560.
doi: 10.1002/cpa.3160450502. |
[15] |
I. Daubechies, M. Defrise and C. D. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Comm. Pure Appl. Math., 57 (2004), 1413-1457.
doi: 10.1002/cpa.20042. |
[16] |
A. H. Delaney and Y. Bresler, Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography, IEEE Trans. Image Process, 7 (1998), 204-221.
doi: 10.1109/83.660997. |
[17] |
G. Demoment, Image reconstruction and restoration: Overview of common estimation structures and problems, IEEE Trans. Acoust. Speech Signal Process, 37 (1989), 2024-2036.
doi: 10.1109/29.45551. |
[18] |
R. DeVore, Nonlinear approximation, Acta Numer., 7 (1998), 51-150.
doi: 10.1017/S0962492900002816. |
[19] |
D. Dobson and F. Santosa, Recovery of blocky images from noisy and blurred data, SIAM J. Appl. Math., 56 (1996), 1181-1198.
doi: 10.1137/S003613999427560X. |
[20] |
Y. Dong, M. Hintermüller and M. Neri, An efficient primal-dual method for $L^1$-TV image restoration, SIAM J. Imaging Sci., 2 (2009), 1168-1189.
doi: 10.1137/090758490. |
[21] |
H. Farid and E. Simoncelli, Differentiation of discrete multidimensional signals, IEEE Trans. Image Process., 13 (2004), 496-508.
doi: 10.1109/TIP.2004.823819. |
[22] |
D. Geman and G. Reynolds, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Machine Intell., 14 (1992), 367-383.
doi: 10.1109/34.120331. |
[23] |
S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell., 6 (1984), 721-741.
doi: 10.1109/TPAMI.1984.4767596. |
[24] |
S. Geman and D. McClure, Bayesian image analysis: an application to single photon emission tomography, in Proc. Stat. Comput. Section: Annual meeting of the Amer. Stat. Assoc., Las Vegas, Nevada, 1985, 12-18. |
[25] |
P. Green, Bayesian reconstructions from emission tomography data using a modified EM algorithm, IEEE Trans. Med. Imag., 9 (1990), 84-93.
doi: 10.1109/42.52985. |
[26] |
T. Hebert and R. Leahy, A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors, IEEE Trans. Med. Imag., 8 (1989), 194-202.
doi: 10.1109/42.24868. |
[27] |
T. Hou, S. Wang and H. Qin, Image deconvolution with multi-stage convex relaxation and its perceptual evaluation, IEEE Trans. Image Process., 20 (2011), 1057-7149.
doi: 10.1109/TIP.2011.2150236. |
[28] |
Y. Hu and M. Jacob, Higher degree total variation (HDTV) regularization for image recovery, IEEE Trans. Image Process., 21 (2012), 2559-2571.
doi: 10.1109/TIP.2012.2183143. |
[29] |
J. Idier, Convex half-quadratic criteria and interacting auxiliary variables for image restoration, IEEE Trans. Image Process., 10 (2001), 1001-1009.
doi: 10.1109/83.931094. |
[30] |
K. Ito, B. Jin and T. Takeuchi, A regularization parameter for nonsmooth Tikhonov regularization, SIAM J. Sci. Comput., 33 (2011), 1415-1438.
doi: 10.1137/100790756. |
[31] |
P. Kohli, A. Shekhovtsov, C. Rother, V. Kolmogorov and P. Torr, On partial optimality in multi-label MRFs, in Proc. $25^{th}$ Int. Conf. Machine Learning, Helsinki, Finland, 2008, 480-487.
doi: 10.1145/1390156.1390217. |
[32] |
S. Lefkimmiatis, A. Bourquard and M. Unser, Hessian-based norm regularization for image restoration with biomedical applications, IEEE Trans. Image Process., 21 (2012), 983-995.
doi: 10.1109/TIP.2011.2168232. |
[33] |
S. Li, On discontinuity-adaptive smoothness priors in computer vision, IEEE Trans. Pattern Anal. Machine Intell., 17 (1995), 576-586.
doi: 10.1109/34.387504. |
[34] |
Y.-R. Li, L. Shen, D.-Q. Dai and B. Suter, Framelet algorithms for de-blurring images corrupted by impulse plus Gaussian noise, IEEE Trans. Image Process., 20 (2011), 1822-1837.
doi: 10.1109/TIP.2010.2103950. |
[35] |
M. Lysaker and X.-C. Tai, Iterative image restoration combining total variation minimization and a second-order functional, Int. J. Comput. Vis., 66 (2006), 5-18.
doi: 10.1007/s11263-005-3219-7. |
[36] |
J. Ma and G. Plonka, The curvelet transform, IEEE Signal Processing Mag., 27 (2010), 118-133.
doi: 10.1109/MSP.2009.935453. |
[37] |
S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Machine Intell., 11 (1989), 674-693.
doi: 10.1109/34.192463. |
[38] |
J. Marroquin, S. Mitter and T. Poggio, Probabilistic solution of ill-posed problems in computational vision, J. Amer. Statist. Assoc., 82 (1987), 76-89.
doi: 10.1080/01621459.1987.10478393. |
[39] |
R. Meyer, Sufficient conditions for the convergence of monotonic mathematical programming algorithms, J. Comput. System Sci., 12 (1976), 108-121.
doi: 10.1016/S0022-0000(76)80021-9. |
[40] |
P. Moulin and J. Liu, Analysis of multiresolution image denoising schemes using generalized gaussian and complexity priors, IEEE Trans. Inform. Theory, 45 (1999), 909-919.
doi: 10.1109/18.761332. |
[41] |
M. Nikolova, A variational approach to remove outliers and impulse noise, J. Math. Imaging Vis., 20 (2004), 99-120.
doi: 10.1023/B:JMIV.0000011920.58935.9c. |
[42] |
M. Nikolova, Analysis of the recovery of edges in images and signals by minimizing nonconvex regularized least-squares, Multiscale Model. Simul., 4 (2005), 960-991.
doi: 10.1137/040619582. |
[43] |
M. Nikolova, M. Ng and C.-P. Tam, Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction, IEEE Trans. Image Process., 19 (2010), 3073-3088.
doi: 10.1109/TIP.2010.2052275. |
[44] |
S. Ramani, T. Blu and M. Unser, Monte-Carlo SURE: a black-box optimization of regularization parameters for general denoising algorithms, IEEE Trans. Image Process., 17 (2008), 1540-1554.
doi: 10.1109/TIP.2008.2001404. |
[45] |
M. C. Robini and I. Magnin, Optimization by stochastic continuation, SIAM J. Imaging Sci., 3 (2010), 1096-1121.
doi: 10.1137/090756181. |
[46] |
M. C. Robini, T. Rastello and I. Magnin, Simulated annealing, acceleration techniques and image restoration, IEEE Trans. Image Process., 8 (1999), 1374-1387.
doi: 10.1109/83.791963. |
[47] |
P. Rodríguez and B. Wohlberg, Efficient minimization method for a generalized total variation functional, IEEE Trans. Image Process., 18 (2009), 322-332.
doi: 10.1109/TIP.2008.2008420. |
[48] |
C. Rother, V. Kolmogorov, V. Lempitsky and M. Szummer, Optimizing binary MRFs via extended roof duality, in Proc. IEEE Conf. Comp. Vis. Patt. Recognition, Minneapolis, MN, 2007, 1-8.
doi: 10.1109/CVPR.2007.383203. |
[49] |
X.-C. Tai, J. Hahn and G. Chung, A fast algorithm for Euler's elastica model using augmented Lagrangian method, SIAM J. Imaging Sci., 4 (2011), 313-344.
doi: 10.1137/100803730. |
[50] |
M. Unser, Ten good reasons for using spline wavelets, in Proc. SPIE, Wavelet Applications in Signal and Image Processing V, vol. 3169, 1997, 422-431.
doi: 10.1117/12.292801. |
[51] |
L. Wang, T.-T. Wong, P. Heng and J. Cheng, Template-matching approach to edge detection of volume data, in Proc. Int. Workshop on Medical Imaging and Augmented Reality, Shatin, Hong Kong, 2001, 286-291. |
[52] |
T. Zhang, Analysis of multi-stage convex relaxation for sparse regularization, J. Mach. Learn. Res., 11 (2010), 1081-1107. |
[53] |
W. Zhu and T. Chan, Image denoising using mean curvature of image surface, SIAM J. Imaging Sci., 5 (2012), 1-32.
doi: 10.1137/110822268. |
[54] |
X. Zhu and P. Milanfar, Automatic parameter selection for denoising algorithms using a no-reference measure of image content, IEEE Trans. Image Process., 19 (2010), 3116-3132.
doi: 10.1109/TIP.2010.2052820. |
show all references
References:
[1] |
E. Bae, J. Shi and X.-C. Tai, Graph cuts for curvature based image denoising, IEEE Trans. Image Process., 20 (2011), 1199-1210.
doi: 10.1109/TIP.2010.2090533. |
[2] |
M. Banham and A. Katsaggelos, Digital image restoration, IEEE Signal Processing Mag., 14 (1997), 24-41.
doi: 10.1109/79.581363. |
[3] |
L. Bedini, L. Benvenuti, E. Salerno and A. Tonazzini, A mixed-annealing algorithm for edge preserving image reconstruction using a limited number of projections, Signal Process., 32 (1993), 397-408.
doi: 10.1016/0165-1684(93)90009-Y. |
[4] |
M. Belge, M. Kilmer and E. Miller, Wavelet domain image restoration with adaptive edge-preserving regularization, IEEE Trans. Image Process., 9 (2000), 597-608.
doi: 10.1109/83.841937. |
[5] |
M. Belge, M. Kilmer and E. Miller, Efficient determination of multiple regularization parameters in a generalized L-curve framework, Inverse Problems, 18 (2002), 1161-1183.
doi: 10.1088/0266-5611/18/4/314. |
[6] |
L. Blanc-Féraud, S. Teboul, G. Aubert and M. Barlaud, Nonlinear regularization using constrained edges in image reconstruction, in Proc. IEEE Int. Conf. Image Processing, 2, Lausanne, Switzerland, (1996), 449-452.
doi: 10.1109/ICIP.1996.560882. |
[7] |
J.-F. Cai, R. Chan and M. Nikolova, Fast two-phase image deblurring under impulse noise, J. Math. Imaging Vis., 36 (2010), 46-53.
doi: 10.1007/s10851-009-0169-7. |
[8] |
R. Chan, Y. Dong and M. Hintermüller, An efficient two-phase $L^1$-TV method for restoring blurred images with impulse noise, IEEE Trans. Image Process., 19 (2010), 1731-1739.
doi: 10.1109/TIP.2010.2045148. |
[9] |
T. Chan, S. Kang and J. Shen, Euler's elastica and curvature-based inpainting, SIAM J. Appl. Math., 63 (2002), 564-592.
doi: 10.1137/S0036139901390088. |
[10] |
T. Chan, A. Marquina and P. Mulet, High-order total variation-based image restoration, SIAM J. Sci. Comput., 22 (2000), 503-516.
doi: 10.1137/S1064827598344169. |
[11] |
P. Charbonnier, L. Blanc-Féraud, G. Aubert and M. Barlaud, Deterministic edge-preserving regularization in computed imaging, IEEE Trans. Image Process., 6 (1997), 298-311.
doi: 10.1109/83.551699. |
[12] |
H. Choi and R. Baraniuk, Wavelet statistical models and Besov spaces, in Proc. SPIE, Wavelet Applications in Signal and Image Processing VII, vol. 3813, 1999, 489-501.
doi: 10.1007/978-0-387-21579-2_2. |
[13] |
E. Chouzenoux, J. Idier and S. Moussaoui, A majorize-minimize strategy for subspace optimization applied to image restoration, IEEE Trans. Image Process, 20 (2011), 1517-1528.
doi: 10.1109/TIP.2010.2103083. |
[14] |
A. Cohen, I. Daubechies and J.-C. Feauveau, Biorthogonal bases of compactly supported wavelets, Comm. Pure Appl. Math., 45 (1992), 485-560.
doi: 10.1002/cpa.3160450502. |
[15] |
I. Daubechies, M. Defrise and C. D. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Comm. Pure Appl. Math., 57 (2004), 1413-1457.
doi: 10.1002/cpa.20042. |
[16] |
A. H. Delaney and Y. Bresler, Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography, IEEE Trans. Image Process, 7 (1998), 204-221.
doi: 10.1109/83.660997. |
[17] |
G. Demoment, Image reconstruction and restoration: Overview of common estimation structures and problems, IEEE Trans. Acoust. Speech Signal Process, 37 (1989), 2024-2036.
doi: 10.1109/29.45551. |
[18] |
R. DeVore, Nonlinear approximation, Acta Numer., 7 (1998), 51-150.
doi: 10.1017/S0962492900002816. |
[19] |
D. Dobson and F. Santosa, Recovery of blocky images from noisy and blurred data, SIAM J. Appl. Math., 56 (1996), 1181-1198.
doi: 10.1137/S003613999427560X. |
[20] |
Y. Dong, M. Hintermüller and M. Neri, An efficient primal-dual method for $L^1$-TV image restoration, SIAM J. Imaging Sci., 2 (2009), 1168-1189.
doi: 10.1137/090758490. |
[21] |
H. Farid and E. Simoncelli, Differentiation of discrete multidimensional signals, IEEE Trans. Image Process., 13 (2004), 496-508.
doi: 10.1109/TIP.2004.823819. |
[22] |
D. Geman and G. Reynolds, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Machine Intell., 14 (1992), 367-383.
doi: 10.1109/34.120331. |
[23] |
S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell., 6 (1984), 721-741.
doi: 10.1109/TPAMI.1984.4767596. |
[24] |
S. Geman and D. McClure, Bayesian image analysis: an application to single photon emission tomography, in Proc. Stat. Comput. Section: Annual meeting of the Amer. Stat. Assoc., Las Vegas, Nevada, 1985, 12-18. |
[25] |
P. Green, Bayesian reconstructions from emission tomography data using a modified EM algorithm, IEEE Trans. Med. Imag., 9 (1990), 84-93.
doi: 10.1109/42.52985. |
[26] |
T. Hebert and R. Leahy, A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors, IEEE Trans. Med. Imag., 8 (1989), 194-202.
doi: 10.1109/42.24868. |
[27] |
T. Hou, S. Wang and H. Qin, Image deconvolution with multi-stage convex relaxation and its perceptual evaluation, IEEE Trans. Image Process., 20 (2011), 1057-7149.
doi: 10.1109/TIP.2011.2150236. |
[28] |
Y. Hu and M. Jacob, Higher degree total variation (HDTV) regularization for image recovery, IEEE Trans. Image Process., 21 (2012), 2559-2571.
doi: 10.1109/TIP.2012.2183143. |
[29] |
J. Idier, Convex half-quadratic criteria and interacting auxiliary variables for image restoration, IEEE Trans. Image Process., 10 (2001), 1001-1009.
doi: 10.1109/83.931094. |
[30] |
K. Ito, B. Jin and T. Takeuchi, A regularization parameter for nonsmooth Tikhonov regularization, SIAM J. Sci. Comput., 33 (2011), 1415-1438.
doi: 10.1137/100790756. |
[31] |
P. Kohli, A. Shekhovtsov, C. Rother, V. Kolmogorov and P. Torr, On partial optimality in multi-label MRFs, in Proc. $25^{th}$ Int. Conf. Machine Learning, Helsinki, Finland, 2008, 480-487.
doi: 10.1145/1390156.1390217. |
[32] |
S. Lefkimmiatis, A. Bourquard and M. Unser, Hessian-based norm regularization for image restoration with biomedical applications, IEEE Trans. Image Process., 21 (2012), 983-995.
doi: 10.1109/TIP.2011.2168232. |
[33] |
S. Li, On discontinuity-adaptive smoothness priors in computer vision, IEEE Trans. Pattern Anal. Machine Intell., 17 (1995), 576-586.
doi: 10.1109/34.387504. |
[34] |
Y.-R. Li, L. Shen, D.-Q. Dai and B. Suter, Framelet algorithms for de-blurring images corrupted by impulse plus Gaussian noise, IEEE Trans. Image Process., 20 (2011), 1822-1837.
doi: 10.1109/TIP.2010.2103950. |
[35] |
M. Lysaker and X.-C. Tai, Iterative image restoration combining total variation minimization and a second-order functional, Int. J. Comput. Vis., 66 (2006), 5-18.
doi: 10.1007/s11263-005-3219-7. |
[36] |
J. Ma and G. Plonka, The curvelet transform, IEEE Signal Processing Mag., 27 (2010), 118-133.
doi: 10.1109/MSP.2009.935453. |
[37] |
S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Machine Intell., 11 (1989), 674-693.
doi: 10.1109/34.192463. |
[38] |
J. Marroquin, S. Mitter and T. Poggio, Probabilistic solution of ill-posed problems in computational vision, J. Amer. Statist. Assoc., 82 (1987), 76-89.
doi: 10.1080/01621459.1987.10478393. |
[39] |
R. Meyer, Sufficient conditions for the convergence of monotonic mathematical programming algorithms, J. Comput. System Sci., 12 (1976), 108-121.
doi: 10.1016/S0022-0000(76)80021-9. |
[40] |
P. Moulin and J. Liu, Analysis of multiresolution image denoising schemes using generalized gaussian and complexity priors, IEEE Trans. Inform. Theory, 45 (1999), 909-919.
doi: 10.1109/18.761332. |
[41] |
M. Nikolova, A variational approach to remove outliers and impulse noise, J. Math. Imaging Vis., 20 (2004), 99-120.
doi: 10.1023/B:JMIV.0000011920.58935.9c. |
[42] |
M. Nikolova, Analysis of the recovery of edges in images and signals by minimizing nonconvex regularized least-squares, Multiscale Model. Simul., 4 (2005), 960-991.
doi: 10.1137/040619582. |
[43] |
M. Nikolova, M. Ng and C.-P. Tam, Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction, IEEE Trans. Image Process., 19 (2010), 3073-3088.
doi: 10.1109/TIP.2010.2052275. |
[44] |
S. Ramani, T. Blu and M. Unser, Monte-Carlo SURE: a black-box optimization of regularization parameters for general denoising algorithms, IEEE Trans. Image Process., 17 (2008), 1540-1554.
doi: 10.1109/TIP.2008.2001404. |
[45] |
M. C. Robini and I. Magnin, Optimization by stochastic continuation, SIAM J. Imaging Sci., 3 (2010), 1096-1121.
doi: 10.1137/090756181. |
[46] |
M. C. Robini, T. Rastello and I. Magnin, Simulated annealing, acceleration techniques and image restoration, IEEE Trans. Image Process., 8 (1999), 1374-1387.
doi: 10.1109/83.791963. |
[47] |
P. Rodríguez and B. Wohlberg, Efficient minimization method for a generalized total variation functional, IEEE Trans. Image Process., 18 (2009), 322-332.
doi: 10.1109/TIP.2008.2008420. |
[48] |
C. Rother, V. Kolmogorov, V. Lempitsky and M. Szummer, Optimizing binary MRFs via extended roof duality, in Proc. IEEE Conf. Comp. Vis. Patt. Recognition, Minneapolis, MN, 2007, 1-8.
doi: 10.1109/CVPR.2007.383203. |
[49] |
X.-C. Tai, J. Hahn and G. Chung, A fast algorithm for Euler's elastica model using augmented Lagrangian method, SIAM J. Imaging Sci., 4 (2011), 313-344.
doi: 10.1137/100803730. |
[50] |
M. Unser, Ten good reasons for using spline wavelets, in Proc. SPIE, Wavelet Applications in Signal and Image Processing V, vol. 3169, 1997, 422-431.
doi: 10.1117/12.292801. |
[51] |
L. Wang, T.-T. Wong, P. Heng and J. Cheng, Template-matching approach to edge detection of volume data, in Proc. Int. Workshop on Medical Imaging and Augmented Reality, Shatin, Hong Kong, 2001, 286-291. |
[52] |
T. Zhang, Analysis of multi-stage convex relaxation for sparse regularization, J. Mach. Learn. Res., 11 (2010), 1081-1107. |
[53] |
W. Zhu and T. Chan, Image denoising using mean curvature of image surface, SIAM J. Imaging Sci., 5 (2012), 1-32.
doi: 10.1137/110822268. |
[54] |
X. Zhu and P. Milanfar, Automatic parameter selection for denoising algorithms using a no-reference measure of image content, IEEE Trans. Image Process., 19 (2010), 3116-3132.
doi: 10.1109/TIP.2010.2052820. |
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