Abstract
Most of blur kernel estimation models may fail when the blurred image contains some complex structures or is contaminated by large blur. In this paper, we propose a hybrid order l 0-regularized blur kernel estimation model for solving the problem. Firstly, we regularize the latent image in a hybrid order case involving both first-order and second-order regularization term, in which l 0 sparse prior is introduced. Secondly, we introduce an improved adaptive adjustment factor into the model for removing detrimental structures and obtaining more useful information. Finally, we develop an efficient optimization algorithm based on the half-quadratic splitting technique to obtain an accurate blur kernel. Extensive experiments results on both synthetic and some challenged real-life images show that proposed model can estimate a more accurate blur kernel and can effectively recover the latent image when it contains complex structures or is contaminated by large blur.
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References
Chan, T.F., Wong, C.K.: Total variation blind deconvolution. IEEE Trans. Image Proces. 7, 370–375 (1998)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (TOG) 28, 145 (2009)
Li, W., Chen, R., Xu, S., et al.: Blind motion image deblurring using nonconvex higher-order total variation model. J. Electron. Imaging 25, 053033 (2016)
Fergus, R., Singh, B., Hertzmann, A., et al.: Removing camera shake from a single photograph. ACM Trans. Graph. (TOG) 25, 787–794 (2006)
Levin, A., Weiss, Y., Durand, F., et al.: Understanding and evaluating blind deconvolution algorithms. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 964–1971 (2009)
Levin, A., Weiss, Y., Durand, F., et al.: Efficient marginal likelihood optimization in blind deconvolution. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 2657–2664. IEEE Computer Society (2011)
Goldstein, A., Fattal, R.: Blur-Kernel estimation from spectral irregularities. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 622–635. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4_45
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. (TOG) 27, 73 (2008)
Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15549-9_12
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 233–240 (2011)
Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1107–1114 (2013)
Lefkimmiatis, S., Bourquard, A., Unser, M.: Hessian-based norm regularization for image restoration with biomedical applications. IEEE Trans. Image Process. 21, 983–995 (2012)
Pan, J., Liu, R., Su, Z., et al.: Kernel estimation from salient structure for robust motion deblurring. Signal Process.: Image Commun. 28, 1156–1170 (2013)
Xu, L., Lu, C., Xu, Y., et al.: Image smoothing via L 0 gradient minimization. ACM Trans. Graph. (TOG) 30, 174 (2011)
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: Advances in Neural Information Processing Systems, pp. 1033–1041 (2009)
Wang, K., Shen, Y., Xiao, L., Wei, Z., Sheng, L.: Blind motion deblurring based on fused ℓ0-ℓ1 regularization. In: Zhang, Y.-J. (ed.) ICIG 2015. LNCS, vol. 9218, pp. 1–10. Springer, Cham (2015). doi:10.1007/978-3-319-21963-9_1
Acknowledgement
This work was supported by the National Science and Technology Program for Public Wellbeing, China (Grant No. 2013GS500303).
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Li, W., Chen, Y., Chen, R., Gong, W., Zhao, B. (2017). Hybrid Order l 0-Regularized Blur Kernel Estimation Model for Image Blind Deblurring. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_29
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DOI: https://doi.org/10.1007/978-3-319-59081-3_29
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