• Laser & Optoelectronics Progress
  • Vol. 55, Issue 4, 041015 (2018)
Can Wang1、2, Fan Yang1、2、*, and Jing Li1、2
Author Affiliations
  • 1 School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
  • 2 Tianjin Key Laboratory of Electronic Materials and Devices, Tianjin 300401, China
  • show less
    DOI: 10.3788/LOP55.041015 Cite this Article Set citation alerts
    Can Wang, Fan Yang, Jing Li. Blind Recovery Method of Motion Blurred Image Based on Combining l1/l2 Norm with High Order and Low Order Total Variation[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041015 Copy Citation Text show less
    References

    [1] Liu R W, Wu D, Wu C S et al. Hybrid regularized blur kernel estimation for single-image blind deconvolution[C]. IEEE International Conference on Systems, Man, and Cybernetics, 1815-1820(2015).

    [2] Li Z M, Zheng Y, Jing W F et al. Hyper-Laplacian non-blind deblurring model based on regional division[C]. International Conference on Network and Information Systems for Computers, 223-226(2015).

    [3] Yang Y M, Gao M T, He J. Wiener filtering image restoration technology research and improvement[J]. Science Technology and Engineering, 12, 7611-7615(2012).

    [4] Zhao B, Zhang W S, Ding H. Novel image deblurring algorithm based on Richardson-Lucy[J]. Computer Engineering and Applications, 47, 1-4(2011).

    [5] Fergus R, Singh B, Hertzmann A et al. Removing camera shake from a single photograph[J]. ACM Transactions on Graphics, 25, 787-794(2006). http://dl.acm.org/citation.cfm?id=1141956

    [6] Shan Q, Jia J Y, Agarwala A. High-quality motion deblurring from a single image[J]. ACM Transactions on Graphics, 27, 73(2008). http://dl.acm.org/citation.cfm?id=1360672

    [7] Yan J W, Xie T T, Peng H et al. Motion image deblurring based on L0 norms regularization term[J]. Laser & Optoelectronics Progress, 54, 021005(2017).

    [8] Perrone D, Favaro P. Alogarithmic image prior for blind deconvolution[J]. International Journal of Computer Vision, 117, 159-172(2016). http://link.springer.com/article/10.1007/s11263-015-0857-2

    [9] Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure[C]. IEEE Conference on Computer Vision and Pattern Recognition, 233-240(2011).

    [10] Su C, Fu T J, Zhang X X et al. An adaptive weighted blind image restoration algorithm based on energy constraint[J]. Acta Optica Sinica, 38, 0210001(2018).

    [11] Wang M, Liu K X, Liu L et al. Super-resolution reconstruction of image based on optimized convolution neural network[J]. Laser & Optoelectronics Progress, 54, 111005(2017).

    [12] Tang J L, Chen Z B, Su B H et al. Super resolution restoration of low quality face images[J]. Laser & Optoelectronics Progress, 55, 031007(2018).

    [13] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[C]. Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics, 60, 259-268(1992).

    [14] Lysaker M, Lundervold A, Tai X C. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time[J]. IEEE Transactions on Image Processing, 12, 1579-1590(2003). http://dl.acm.org/citation.cfm?id=2320489

    [15] Papafitsoros K, Schönlieb C B. A combined first and second order variational approach for image reconstruction[J]. Journal of Mathematical Imaging and Vision, 48, 308-338(2014). http://link.springer.com/article/10.1007/s10851-013-0445-4

    [16] Jiang B Y, Zhang J. Denosing model base on double-fidelity total variation[J]. Laser & Optoelectronics Progress, 55, 021004(2018).

    [17] Krishnan D, Fergus R. Fast image deconvolution using hyper-Laplacian priors[C]. Proceedings of the 22 nd International Conference on Neural Information Processing Systems, 1033-1041(2009).

    [18] Tang S, Gong W G, Li W H et al. Non-blind image deblurring method by local and nonlocal total variation models[J]. Signal Processing, 94, 339-349(2014). http://dl.acm.org/citation.cfm?id=2537217&preflayout=tabs

    [19] Xu L, Jia J Y. Two-phase kernel estimation for robust motion deblurring[C]. European Conference on Computer Vision, 157-170(2010).

    Can Wang, Fan Yang, Jing Li. Blind Recovery Method of Motion Blurred Image Based on Combining l1/l2 Norm with High Order and Low Order Total Variation[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041015
    Download Citation