Abstract
In this paper we propose a novel \(L_0\) penalty function of both gradient and image itself as the regular term in the total energy function. This regular term is based on sparse prior and solved as part of mathematical optimization problem. Our method not only reserves structure information of the image but also avoids over smooth in the final restoration. We illustrate the applicability and validity of our method through experiments on both synthetic and natural blurry images. Despite we don’t have numerous iterations, the convergence rate and result quality outperform the most state-of-the-art methods.
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References
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (TOG) 28, 145 (2009). ACM
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. (TOG) 27, 73 (2008). ACM
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. (TOG) 25, 787–794 (2006). ACM
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 233–240. IEEE (2011)
Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Perrone, D., Favaro, P.: Total variation blind deconvolution: the devil is in the details. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2909–2916. IEEE (2014)
Zuo, W., Ren, D., Gu, S., Lin, L., Zhang, L.: Discriminative learning of iteration-wise priors for blind deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3232–3240 (2015)
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via \(L_0\) gradient minimization. ACM Trans. Graph. (TOG) 30, 174 (2011). ACM
Pan, J., Hu, Z., Su, Z., Yang, M.H.: Deblurring text images via \(L_0\)-regularized intensity and gradient prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)
Hu, Z., Yang, M.-H.: Good regions to deblur. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 59–72. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33715-4_5
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1964–1971. IEEE (2009)
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
Zhong, L., Cho, S., Metaxas, D., Paris, S., Wang, J.: Handling noise in single image deblurring using directional filters. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 612–619 (2013)
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: Advances in Neural Information Processing Systems, pp. 1033–1041 (2009)
Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.: Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 27–40. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33786-4_3
Acknowledgements
The work described in this paper was supported by Zhejiang Provincial Natural Science Foundation of China under Grant number LY15F020031 and LQ16F030007, National Natural Science Foundation of China (NSFC) under Grant numbers 11302195 and 61401397.
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Song, H., Liu, S. (2016). \(L_0\)-Regularization Based on Sparse Prior for Image Deblurring. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_4
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DOI: https://doi.org/10.1007/978-981-10-3476-3_4
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