Improved TV for blind deblurring with split Bregman iteration

References

• Chen, H., Wang, C., Song, Y. and Li, Z. Split Bregmanized anisotropic total variation model for image deblurring. J. Vis. Commun. Image Represent., 2015, 31, 282–293. doi: 10.1016/j.jvcir.2015.07.004
   Web of Science ®Google Scholar
• Helstrom, C. Image restoration by the method of least squares. J. Opt. Soc. Am., 1967, 57, (3), 297–303. doi: 10.1364/JOSA.57.000297
   Web of Science ®Google Scholar
• Richardson, W. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., 1972, 62, (1), 55–59. doi: 10.1364/JOSA.62.000055
   Web of Science ®Google Scholar
• Lucy, L. An iterative technique for the rectification of observed distributions. Astron. J., 1974, 79, (6), 745–754. doi: 10.1086/111605
   Web of Science ®Google Scholar
• Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T. and Freeman, W. T. Removing camera shake from a single photograph. ACM Trans. Graphics, 2006, 25, (3), 787–794. doi: 10.1145/1141911.1141956
   Web of Science ®Google Scholar
• Shan, Q., Jia, J. and Agarwala, A. High-quality motion deblurring from a single image. ACM Trans. Graphics, 2008, 27, (3), 73:1–73:10. doi: 10.1145/1360612.1360672
   Google Scholar
• Chan, T. F. and Wong, C.-K. Total variation blind deconvolution. IEEE Trans. Image Process., 1998, 7, (3), 370–375. doi: 10.1109/83.661187
   PubMed Web of Science ®Google Scholar
• Zuo, W. and Lin, Z. A generalized accelerated proximal gradient approach for total-variation-based image restoration. IEEE Trans. Image Process., 2011, 20, (10), 2748–2759. doi: 10.1109/TIP.2011.2131665
   PubMed Web of Science ®Google Scholar
• Wang, Y., Yang, J., Yin, W. and Zhang, Y. A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci., 2008, 1, (3), 248–272. doi: 10.1137/080724265
   Web of Science ®Google Scholar
• Yang, J., Zhang, Y. and Yin, W. An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise. SIAM J. Sci. Comput., 2009, 31, (4), 2842–2865. doi: 10.1137/080732894
   Web of Science ®Google Scholar
• Li, X., Pan, J., Lin, Y. and Su, Z. Fast blind deblurring via normalized sparsity prior. J. Inf. Comput. Sci., 2013, 10, (16), 5083–5091. doi: 10.12733/jics20102362
   Google Scholar
• Krishnan, D., Tay, T. and Fergus, R. Blind deconvolution using a normalized sparsity measure. IEEE Conf. on ‘Computer Vision and Pattern Recognition (CVPR)’, Providence, RI, 20–25 June 2011 (IEEE), pp. 233–240.
   Google Scholar
• Liu, Y. Blind deblurring based on Locally Weighted Total Variation and continuous blur kernel. Dalian University of Technology, 2014, 4, 33–43.
   Google Scholar
• Goldstein, T. and Osher, S. The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci., 2009, 2, (2), 323–343. doi: 10.1137/080725891
   Web of Science ®Google Scholar
• Li, W., Li, Q., Gong, W. and Tang, S. Total variation blind deconvolution employing split Bregman iteration. J. Vis. Commun. Image Represent., 2012, 23, (3), 409–417. doi: 10.1016/j.jvcir.2011.12.003
   Web of Science ®Google Scholar
• Osher, S., Burger, M., Goldfarb, D., Xu, J. and Yin, W. An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul., 2005, 4, (2), 460–489. doi: 10.1137/040605412
   Web of Science ®Google Scholar
• Yin, W., Osher, S., Goldfarb, D. and Darbon, J. Bregman iterative algorithms for L1-minimization with applications to compressed sensing. SIAM J. Imaging Sci., 2008, 1, (1), 143–168. doi: 10.1137/070703983
   Web of Science ®Google Scholar
• Cho, S. and Lee, S. Fast motion deblurring. ACM Trans. Graphics, 2009, 28, (5), 145:1–145:8. doi: 10.1145/1618452.1618491
   Google Scholar
• Xu, L. and Jia, J. Two-phase kernel estimation for robust motion deblurring. Proc. 11th Eur. Conf. on ‘Computer Vision', Part I, Heraklion, Crete, Greece, 5–11 September 2010 (Springer, Berlin), 157–170.
   Google Scholar
• Xu, L., Lu, C., Xu, Y. and Jia, J. Image smoothing via l0 gradient minimization. ACM Trans. Graphics, 2011, 30, (6), 174:1–174:12.
   Google Scholar
• Pan, J., Liu, R., Su, Z. and Gu, X. Kernel estimation from salient structure for robust motion deblurring. Signal Process. Image Commun., 2012, 28, (9), 1156–1170. arXiv:1212.1073.
   Google Scholar
• Rudin, L. I., Osher, S. and Fatemi, E. Nonlinear total variation based noise removal algorithms. Physica D, 1992, 60, 259–268. doi: 10.1016/0167-2789(92)90242-F
   Web of Science ®Google Scholar
• Yuan, Q., Zhang, L. and Shen, H. Multiframe super-resolution employing a spatially weighted total variation model. IEEE Trans. Circuits Syst. Video Technol., 2012, 22, (3), 379–392. doi: 10.1109/TCSVT.2011.2163447
   Web of Science ®Google Scholar
• Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process., 2004, 13, (4), 600–612. doi: 10.1109/TIP.2003.819861
   PubMed Web of Science ®Google Scholar