基于Radon变换的运动模糊图像恢复

doi:10.11772/j.issn.1001-9081.2014.07.2005

计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 2005-2009.DOI: 10.11772/j.issn.1001-9081.2014.07.2005

基于Radon变换的运动模糊图像恢复

廖永忠¹,²,蔡自兴²,何湘华¹

1. 湖南第一师范学院信息科学与工程系，长沙 410205
2. 中南大学信息科学与工程学院，长沙 410081

收稿日期:2014-01-15 修回日期:2014-03-10 出版日期:2014-07-01 发布日期:2014-08-01
通讯作者: 廖永忠
作者简介:廖永忠(1975-),男,湖南常德人,副教授，博士研究生,主要研究方向：信号处理；蔡自兴(1938-),男,福建莆田人，教授,博士生导师,主要研究方向：智能控制、模式识别；何湘华(1977-),女, 湖南常德人，硕士，主要研究方向：多媒体技术。
基金资助:
国家自然科学基金资助项目;湖南省教育厅一般科研项目

Motion blurred image blind restoration based on Radon transform

LIAO Yongzhong¹,²,CAI Zixing²,HE Xianghua¹

1. Department of Information Science and Engineering, Hunan First Normal University, Changsha Hunan 410205, China
2. School of Information Science and Engineering, Central South University, Changsha Hunan 410002, China;

Received:2014-01-15 Revised:2014-03-10 Online:2014-07-01 Published:2014-08-01
Contact: LIAO Yongzhong

摘要/Abstract

摘要：

提出了一种快速自然运动模糊图像恢复算法，采用一种新的基于Radon变换算法来确定模糊核函数；在确定模糊核函数后，对于模糊图像的恢复采用了一种改进的基于l1范数和l2范数混合保真项的变分图像恢复算法。实验结果表明,与Fergus的算法和Levinss的算法比较，所提算法对于一类线性运动占主要因素的强噪声模糊图像的恢复具有更快的速度和良好的恢复效果。

Abstract:

In this paper, a fast blind restoration algorithm for motion blurred image was proposed, using a robust algorithm based on Radon transform-domain to determine the blur kernel function, then a modified total variation algorithm was used to restore the blurred images. Its cost function is the sum of three terms corresponding to total variation l2-norm regularization, least squares fidelity term and l1-norm fidelity term. Compared with Fergus' and Levin' algorithm, the experiment results show that the algorithm for a class of motion blurred image caused by the linear movement parallel to the lens has higher speed and good recovery effect.

中图分类号:

TP391.4

廖永忠蔡自兴何湘华. 基于Radon变换的运动模糊图像恢复[J]. 计算机应用, 2014, 34(7): 2005-2009.

LIAO Yongzhong CAI Zixing HE Xianghua. Motion blurred image blind restoration based on Radon transform[J]. Journal of Computer Applications, 2014, 34(7): 2005-2009.

参考文献

［1］ZOU M. Deconvolution and signal recovery［M］. Beijing: National Defence Industry Press, 2001:185-186.(邹谋炎.反卷积和信号复原［M］.北京:国防工业出版社,2001:185-186.)
［2］FERGUS R, SINGH B, HERTZMANN A, et al. Removing camera shake from a single photograph［J］.ACM Transactions on Graphics, 2006, 25(3): 787-794.
［3］LEVIN A, WEISS Y, DURAND F, et al. Efficient marginal likelihood optimization in blind deconvolution［C］// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011: 2657-2664.
［4］SHAN Q, JIA J, AGARWALA A. High-quality motion deblurring from a single image［J］.ACM Transactions on Graphics, 2008, 27(3): 73.
［5］XU L, JIA J. Two-phase kernel estimation for robust motion deblurring［C］// Computer Vision-ECCV 2010. Berlin: Springer, 2010: 157-170.
［6］CHO T S, PARIS S, HORN B K P, et al. Blur kernel estimation using the Radon transform［C］// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011: 241-248.
［7］ZHONG L, CHO S, METAXAS D, et al. Handling noise in single image deblurring using directional filters［C］// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2013: 612-619.
［8］DOBE M, MACHALA L, FRST T. Blurred image restoration: A fast method of finding the motion length and angle［J］. Digital Signal Processing, 2010, 20(6): 1677-1686.
［9］SAKANO M, SUETAKE N, UCHINO E. A PSF estimation based on Hough transform concerning gradient vector for noisy and motion blurred images［J］. IEICE Transactions on Information and Systems, 2007, 90(1): 182-190.
［10］LIAO Y,CAI Z,HE X. Fast algorithm for motion blurred image blind deconvolution［J］. Optics and Precision Engineering, 2013,21(10): 2688-2695.(廖永忠,蔡自兴,何湘华.快速运动模糊图像盲解卷积算法［J］.光学精密工程,2013,21(10)：2688-2695.)
［11］CARASSO A S. Direct blind deconvolution［J］. SIAM Journal on Applied Mathematics, 2001, 61(6): 1980-2007.
［12］MOGHADDAM M E, JAMZAD M. Motion blur identification in noisy images using fuzzy sets［C］// Proceedings of the 5th IEEE International Symposium on Signal Processing and Information Technology. Piscataway: IEEE, 2005: 862-866.
［13］MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics［C］// Proceedings of the 8th IEEE International Conference on Computer Vision. Piscataway: IEEE, 2001: 416-423.
［14］KRAHMER F, LIN Y, MCADOO B, et al. Blind image deconvolution: Motion blur estimation［EB/OL］. ［2013-10-10］.https://www.ima.umn.edu/2005-2006/MM8.9-18.06/activities/Wohlberg-Brendt/team5_rep.pdf.
［15］RICHARDSON W H. Bayesian-based iterative method of image restoration［J］. Journal of the Optical Society of America, 1972, 62(1): 55-59.
［16］MIGNOTTE M. A segmentation-based regularization term for image deconvolution［J］. IEEE Transactions on Image Processing, 2006, 15(7): 1973-1984.
［17］SROUBEK F, MILANFAR P. Robust multichannel blind deconvolution via fast alternating minimization［J］. IEEE Transactions on Image Processing, 2012, 21(4): 1687-1700.
［18］SHI Y, YANG X. New total variation regularized L1 model for image restoration［J］. Digital Signal Processing, 2010, 20(6): 1656-1676.
［19］RODRíGUEZ P, WOHLBERG B. A generalized vector-valued total variation algorithm［C］// Proceedings of the 16th IEEE International Conference on Image Processing. Piscataway: IEEE, 2009: 1309-1312.

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基于Radon变换的运动模糊图像恢复

Motion blurred image blind restoration based on Radon transform

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