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Since single image restoration algorithms using lots of priori information lead to high complexity and low computational efficiency, a gradient cepstrum analysis method is proposed to estimate the point spread function PSF for a single blurred image. Firstly, we present the basic principle of estimating PSF from gradient cepstrum of a single blurred image and use the phase retrieval algorithm to recover phase information of the two-dimensional PSF, which can obtain the estimated PSF rapidly, Secondly, to evaluate the accuracy of the proposed PSF estimation method, the total variation regularized image restoration model coupling with an image gradient fidelity term is established and an alternating direction method with rapid and stable convergence is adopted to optimize the energy function. Both synthetic and real blurred images are tested to verify the performance of our scheme. Results show that our scheme not only can estimate the PSF rapidly and accurately so that it overcomes shortcomings of traditional algorithm with slow convergence, but also suppresses ringing effects to preserve information in edges. These advantages provide theoretical and technical foundation of the real engineering requirement in single image deblurring, especially for large scale images.
[1] Zhao L, Jin W Q, Chen Y N, Su B H 2008 Acta Opt. Sin. 28 1703 (in Chinese) [赵琳, 金伟其, 陈翼男, 苏秉华 2008 光学学报 28 1703]
[2] Sun H, Li Z Q 2012 Chinese Opt. 5 174 (in Chinese) [孙辉, 李志强 2012 中国光学 5 174]
[3] [4] [5] Chen Y N, Jin W Q, Zhao L, Zhao L 2009 Acta Phys. Sin. 58 264 (in Chinese) [陈翼男, 金伟其, 赵磊, 赵琳 2009 58 264]
[6] Sun Z G, Han C Z 2010 Acta Phys. Sin. 59 998 (in Chinese) [孙增国, 韩崇昭 2010 59 998]
[7] [8] Fergus R, Singh B, Hertzmann A, Roweis S T, Freeman W 2006 ACM T. Graph. 25 787
[9] [10] [11] Qi S, Jia J Y, Agrwala A 2008 ACM T. Graph. 27 73
[12] Krishnan D, Fergus R 2009 Advances in Neural Inf. Proc. Sys. 22 1033
[13] [14] [15] Levin A, Wesis Y, Durand F, Freeman W T 2011 Proceedings of the 24th IEEE Conference on Computer Vision and Partern Recognition Colorado Springs, USA, June 20-25, 2011 p2657
[16] Zou W J 2010 Journal of University of South China (Science and Technology) 24 61 (in Chinese) [邹文杰 2010 南华大学学报 (自然科学版) 24 61]
[17] [18] Shi X Z 1986 Journal of Astronautic Metrology and Measurement 30 48 (in Chinese) [史习智 1986 宇航计测技术 30 48]
[19] [20] Li N, Lu X B 2011 Chinese Journal of Scientific Instrument 32 2523 (in Chinese) [李楠, 路小波 2011 仪器仪表学报 32 2523]
[21] [22] Hu W, Xue J R, Zheng N N 2012 IEEE T. Image Process 21 386
[23] [24] [25] Li F 2012 Acta Phys. Sin. 61 230203 (in Chinese) [李斐 2012 61 230203]
[26] Yuan X M, Yang J F 2013 Pac. J. Optim. 9 167
[27] -
[1] Zhao L, Jin W Q, Chen Y N, Su B H 2008 Acta Opt. Sin. 28 1703 (in Chinese) [赵琳, 金伟其, 陈翼男, 苏秉华 2008 光学学报 28 1703]
[2] Sun H, Li Z Q 2012 Chinese Opt. 5 174 (in Chinese) [孙辉, 李志强 2012 中国光学 5 174]
[3] [4] [5] Chen Y N, Jin W Q, Zhao L, Zhao L 2009 Acta Phys. Sin. 58 264 (in Chinese) [陈翼男, 金伟其, 赵磊, 赵琳 2009 58 264]
[6] Sun Z G, Han C Z 2010 Acta Phys. Sin. 59 998 (in Chinese) [孙增国, 韩崇昭 2010 59 998]
[7] [8] Fergus R, Singh B, Hertzmann A, Roweis S T, Freeman W 2006 ACM T. Graph. 25 787
[9] [10] [11] Qi S, Jia J Y, Agrwala A 2008 ACM T. Graph. 27 73
[12] Krishnan D, Fergus R 2009 Advances in Neural Inf. Proc. Sys. 22 1033
[13] [14] [15] Levin A, Wesis Y, Durand F, Freeman W T 2011 Proceedings of the 24th IEEE Conference on Computer Vision and Partern Recognition Colorado Springs, USA, June 20-25, 2011 p2657
[16] Zou W J 2010 Journal of University of South China (Science and Technology) 24 61 (in Chinese) [邹文杰 2010 南华大学学报 (自然科学版) 24 61]
[17] [18] Shi X Z 1986 Journal of Astronautic Metrology and Measurement 30 48 (in Chinese) [史习智 1986 宇航计测技术 30 48]
[19] [20] Li N, Lu X B 2011 Chinese Journal of Scientific Instrument 32 2523 (in Chinese) [李楠, 路小波 2011 仪器仪表学报 32 2523]
[21] [22] Hu W, Xue J R, Zheng N N 2012 IEEE T. Image Process 21 386
[23] [24] [25] Li F 2012 Acta Phys. Sin. 61 230203 (in Chinese) [李斐 2012 61 230203]
[26] Yuan X M, Yang J F 2013 Pac. J. Optim. 9 167
[27]
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