Lucy B. An iterative technique for the rectification of observed distributions. Astronomical Journal, 1974, 79(6): 745-754
[2]
Richardson W H. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America, 1972, 62(1): 55-59
[3]
Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors. In: Proceedings of the 2009 Advances in Neural Information Processing Systems. Vancouver, British Columbia: Curran Associates, Inc., 2009. 1033- 1041
[4]
Galatsanos N P, Katsaggelos A K. Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation. IEEE Transactions on Image Processing, 1992, 1(3): 322-336
[5]
Wang W L, Yang J F, Yin W T, Zhang Y. A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences, 2008, 1(3): 248- 272
[6]
Babacan S D, Molina R, Katsaggelos A K. Variational Bayesian blind deconvolution using a total variation prior. IEEE Transactions on Image Processing, 2009, 18(1): 12- 26
[7]
He Chuan, Hu Chang-Hua, Zhang Wei, Shi Biao. Box-constrained total-variation image restoration with automatic parameter estimation. Acta Automatica Sinica, 2014, 40(8): 1804-1811(何川, 胡昌华, 张伟, 师彪. 区间约束的全变差图像复原和自动参数估计. 自动化学报, 2014, 40(8): 1804-1811)
[8]
Bioucas-Dias J M. Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors. IEEE Transactions on Image Processing, 2006, 15(4): 937-951
[9]
Zhang H, Zhang Y N. Sparse representation based iterative incremental image deblurring. In: Proceedings of the 16th IEEE International Conference on Image Processing (ICIP). Cairo, Egypt: IEEE, 2009. 1293-1296
[10]
Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D, 1992, 60(1-4): 259- 268
[11]
Kristian B, Karl K, Thomas P. Total generalized variation. SIAM Journal on Imaging Sciences, 2010, 3(3): 492-526
[12]
Knoll1 F, Bredies K, Pock T, Stollberger R. Second order total generalized variation (TGV) for MRI. Magnetic Resonance in Medicine, 2011, 65(2): 480-491
[13]
Yin W T, Osher S, Goldfarb D, Darbon J. Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 2008, 1(1): 143-168
[14]
Goldstein T, Osher S. The split Bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences, 2009, 2(2): 323-343
[15]
Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202
[16]
Puig A T, Wiesel A, Fleury G, Hero A O. Multidimensional shrinkage-thresholding operator and group LASSO penalties. IEEE Signal Processing Letters, 2011, 18(6): 363-366
[17]
Oliveira J P, Bioucas-Dias J M, Figueiredo M A T. Adaptive total variation image deblurring: a majorization-minimization approach. Signal Processing, 2009, 89(9): 1683-1693
