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实现有效的单幅散焦图像盲复原对军事及地质勘测领域的清晰图像获取具有极为重要的意义.常用算法存在计算量大、振铃及噪声敏感的问题，为此本文提出了贝叶斯框架下迭代双边滤波器的快速盲复原算法.它首先用基于深度信息的盲去卷积结果估计点扩散函数的概率模型，进而通过贝叶斯理论构建合理的盲复原最小优化问题；然后推理分析最小优化问题的求解实质，得出双边滤波器快速求解最小优化问题的结论；最后设计迭代联合双边滤波器的求解方式，即利用一次双边滤波器求解的复原结果设计联合双边滤波器的指导图，再将其作为优化问题的输入，迭代实施求解.实验结果表明：该算法能有效抑制振铃，减少计算量，去除噪声，85%图像的像素误差平均值低于0.03，较常用盲去卷积法在同一误差区间的复原成功率提高了19%，运行时间缩短了约78%，能有效用于单幅散焦图像盲复原的实际工程实践中.It is significant to realize effective defocus image restoration for acquiring clear image in military and geological examination field. Most of existing algorithms have the problems of large computational cost, ringing and noise sensitivity, hence a novel approach by iterative joint bilateral filtering under Bayesian framework is proposed. Firstly, it utilizes defocus image depth estimation to compute the point spread function in the Bayesian framework. Then a minimum optimization problem is built to represent the blind restoration problem. After inferencing the solution procedure of the minimum optimization problem, we find that the joint bilateral filters can be used to search the optimal solution, which not only simplifies the searching procedure but also reduces the computational cost. Finally, an iterative joint bilateral filtering is designed to realize the image restoration. That means that the original restored image obtained from the bilateral filtering is used to design the guide image for the joint bilateral filters, and the guide image will serve as the input of the optimization problem for acquiring the better optimal result. This procedure is repeated until convergence. The experimental results indicate that this method can yield the ringing, reduce the computational cost, and remove the noise. Generally speaking, the average pixel error of 85% images is under 0.03, which has improved 19% comparing with the same error rang of existing algorithms, and 78% shorter than those of compared algorithms. It can be used in the engineering practice of blind restoration for single defocus image.
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[1] Schuon S, Diepold K 2009 Acta Astronaut. 64 1050
[2] Gupta P, Mehra R 2015 Int. J. Comput. Appl. 130 20
[3] Escande P, Weiss P, Malgouyres F 2013 J. Phys. 2013 012004
[4] Galdran A, Pardo D, Picón A, Alvarez-Gila A 2015 J. Visual Commun. Image Represent. 26 132
[5] Jin Z L, Han J, Zhang Y, Bai L F 2014 Acta Phys. Sin. 63 069501 (in Chinese)[金左轮, 韩静, 张毅, 柏连发2014 63 069501]
[6] Shi M Z, Xu T F, Liang J, Li X M 2013 Acta Phys. Sin. 62 174204 (in Chinese)[石明珠, 许廷发, 梁炯, 李相民2013 62 174204]
[7] Li X N, Huang H Y, Jia X N, Ma S L 2015 Acta Phys. Sin. 64 134102 (in Chinese)[李鑫楠, 黄贺艳, 贾小宁, 马驷良2015 64 134102]
[8] Lu H M, Xu M, Li X 2014 Acta Opt. Sin. 2014 081002 (in Chinese)[卢惠民, 徐明, 李迅2014光学学报2014 081002]
[9] Tai Y W, Brown M S 2009 In Proc of 16th IEEE International Conf on Image Processing Cairo, Egypt, November 7-10, 2009 p1797
[10] Cai J F, Ji H, Liu C, Shen Z 2009 J. Comput. Phys. 228 5057
[11] Kundur D, Hatzinakos D 1996 IEEE Trans. Signal Process. 13 43
[12] Fahmy M F, Raheem G M A, Mohamed U S, Fahmy O F 2012 J. Signal Inf. Process. 3 98
[13] Almeida M S, Figueiredo M A 2013 IEEE Trans. Image Process. 22 2751
[14] Schmidt U, Schelten K, Roth S 2011 In Proc of 16th IEEE Conf on Computer Vision and Pattern Recognition Colorado, United State, June 21-23, 2011 p2625
[15] Likas A C, Galatsanos N P 2004 IEEE Trans. Signal Process. 52 2222
[16] Zhang H, Wipf D, Zhang Y 2014 IEEE Trans. Pattern Anal. Mach. Intell. 36 1628
[17] Song C, Deng H, Gao H, Zhang H, Zuo W 2016 Neurocomputing 197 95
[18] Cao Y, Fang S, Wang F 2011 In Proc. of 6th International Conf. on Image and Graphics Beijing, China, October 24-26, 2011 p168
[19] Elad M 2005 International Conf on Scale-Space Theories in Computer Vision in Scale Space and PDE Methods in Computer Vision (Berlin Heidelberg:Springer) pp217-229
[20] Huynh T Q, Ghanbari M 2008 Electron. Lett. 44 800
[21] Levin A, Weiss Y, Durand F, Freeman W T 2011 In Proc of 18th IEEE Conf on Computer Vision and Pattern Recognition Colorado, United State, June 21-23, 2011 p2657
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