基于指导滤波的图像盲复原算法

李鑫楠; 黄贺艳; 贾小宁; 马驷良

doi:10.7498/aps.64.134202

摘要

作为图像处理领域的重要分支和研究热点之一, 图像复原方法的研究始终具有重要理论意义和广泛的应用价值, 图像盲复原一直以来都是图像复原中比较困难的问题之一. 针对相机与所拍摄景物之间由于相对位置移动而使所获得图像发生运动模糊的情况, 本文提出了一种基于指导滤波的图像盲复原算法. 我们首先通过频域迭代算法对点扩散函数进行估计. 然后, 由于指导滤波具有较好的保持图像边缘的特性, 我们应用基于指导滤波的图像非盲复原算法恢复目标图像. 对以上两步进行反复迭代, 直到获得最终的清晰图像. 为了验证本文所提算法的有效性, 给出了多组对比实验. 实验结果表明, 本文所提算法能够在有效地抑制噪声和振铃效应的同时, 还能够更好的保持图像的边缘和纹理细节. 因此, 本文算法可以获得更高质量的复原图像.

关键词:

Abstract

As a major branch and research focus in the image processing field, the study on image restoration is always of practical significance and application value. Blind image restoration has always been one of the most difficult problems in image restoration. In view of the image motion blurring induced by the relative motion between the camera and the subject, we present a blind image restoration method based on guided filter. We first estimate the point spread function by using the iteration algorithm in the frequency domain. And then, because the guided filter has the edge-preserving smoothing property, we restore the target image by the non-blind image restoration algorithm based on the guided filter. By iterating the above two steps, we can obtain the original clear image. In order to verify the effectiveness of the proposed algorithm, we give several groups of experiments. Experimental results show that the proposed algorithm can not only effectively eliminate the noise and suppress the ringing, but also well preserve the edge and texture details. Therefore, the proposed algorithm can restore the image with high quality.

Keywords:

作者及机构信息

1.
吉林大学数学学院, 长春 130012;

2.
长春大学理学院, 长春 130022;

3.
长春理工大学理学院, 长春 130022

Authors and contacts

1.
School of Mathematics, Jilin University, Changchun 130012, China;

2.
School of Science, Changchun University, Changchun 130022, China;

3.
School of Science, Changchun University of Science and Technology, Changchun 130022, China

参考文献

[1]	Bishop T E, Babacan S D, Amizic B, Chan T, Molina R, Katsaggelos A K 2007 Blind image deconvolution: theory and applications. CRC Press
[2]	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]
[3]	Shi M Z, Xu T F, Zhang K 2011 Opt. Precision Eng. 19 1973 (in Chinese) [石明珠, 许廷发, 张坤 2011 光学精密工程 19 1973]
[4]	Li F 2012 Acta Phys. Sin. 61 230203 (in Chinese) [李斐 2012 61 230203]
[5]	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]
[6]	Sun H, Li Z Q 2012 Chinese Opt. 5 174 (in Chinese) [孙辉, 李志强 2012 中国光学 5 174]
[7]	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]
[8]	Yang J, Yin W, Zhang Y, Wang Y 2013 SIAM J. Imaging Sciences 2 569
[9]	Michailovich O V 2011 IEEE Trans. Image Process 20 1281
[10]	Huang H Y, Yang H, Ma S L 2013 J. Electronic Imaging 22
[11]	Fergus R, Singh B, Hertzmann A, Roweis S T, Freeman W T 2006 ACM Trans. Graphics 25 787
[12]	Shan Q, Jia J, Agarwala A 2008 ACM Trans. Graphics 27 73
[13]	Levin A, Weiss Y, Durand F 2011 IEEE Trans. Pattern Anal. and Mach. Intell. 33 2354
[14]	Yuan L, Sun J, Quan L, Shum H Y 2007 ACM Trans. Graphics 26 1
[15]	Xu L, Jia J 2010 In Proc. of the European Conf. on Computer Vision
[16]	Babacan S D, Rafael M, Minh X D, Aggelos K K 2012 In Proc. of the European Conf. on Computer Vision 6 341
[17]	Zhong L, Cho S, Dimitris M, Sylvain P, Jue W 2013 In Proc. CVPR
[18]	He K M, Sun J, Tang X 2010 In Proc. of the European Conf. on Computer Vision 1 1
[19]	He K M, Sun J, Tang X 2013 IEEE Trans. Pattern Anal. and Mach. Intell. 35 1397
[20]	Zomet A, Peleg S 2002 Proc. IEEE Workshop Applications of Computer Vision
[21]	He K M, Sun J, Tang X 2009 Proc. IEEE Conf. Computer Vision and Pattern Recognition
[22]	Levin A, Lischinski D, Weiss Y 2006 Proc. IEEE Conf. Computer Vision and Pattern Recognition
[23]	Ayers G R, Dainty J C 1998 Optics Letters 13 547
[24]	Sunghyun C, Seungyong L 2009 ACM Trans. Graphics(SIGGRAPH ASIA) 28 145

施引文献

[1]	Bishop T E, Babacan S D, Amizic B, Chan T, Molina R, Katsaggelos A K 2007 Blind image deconvolution: theory and applications. CRC Press
[2]	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]
[3]	Shi M Z, Xu T F, Zhang K 2011 Opt. Precision Eng. 19 1973 (in Chinese) [石明珠, 许廷发, 张坤 2011 光学精密工程 19 1973]
[4]	Li F 2012 Acta Phys. Sin. 61 230203 (in Chinese) [李斐 2012 61 230203]
[5]	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]
[6]	Sun H, Li Z Q 2012 Chinese Opt. 5 174 (in Chinese) [孙辉, 李志强 2012 中国光学 5 174]
[7]	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]
[8]	Yang J, Yin W, Zhang Y, Wang Y 2013 SIAM J. Imaging Sciences 2 569
[9]	Michailovich O V 2011 IEEE Trans. Image Process 20 1281
[10]	Huang H Y, Yang H, Ma S L 2013 J. Electronic Imaging 22
[11]	Fergus R, Singh B, Hertzmann A, Roweis S T, Freeman W T 2006 ACM Trans. Graphics 25 787
[12]	Shan Q, Jia J, Agarwala A 2008 ACM Trans. Graphics 27 73
[13]	Levin A, Weiss Y, Durand F 2011 IEEE Trans. Pattern Anal. and Mach. Intell. 33 2354
[14]	Yuan L, Sun J, Quan L, Shum H Y 2007 ACM Trans. Graphics 26 1
[15]	Xu L, Jia J 2010 In Proc. of the European Conf. on Computer Vision
[16]	Babacan S D, Rafael M, Minh X D, Aggelos K K 2012 In Proc. of the European Conf. on Computer Vision 6 341
[17]	Zhong L, Cho S, Dimitris M, Sylvain P, Jue W 2013 In Proc. CVPR
[18]	He K M, Sun J, Tang X 2010 In Proc. of the European Conf. on Computer Vision 1 1
[19]	He K M, Sun J, Tang X 2013 IEEE Trans. Pattern Anal. and Mach. Intell. 35 1397
[20]	Zomet A, Peleg S 2002 Proc. IEEE Workshop Applications of Computer Vision
[21]	He K M, Sun J, Tang X 2009 Proc. IEEE Conf. Computer Vision and Pattern Recognition
[22]	Levin A, Lischinski D, Weiss Y 2006 Proc. IEEE Conf. Computer Vision and Pattern Recognition
[23]	Ayers G R, Dainty J C 1998 Optics Letters 13 547
[24]	Sunghyun C, Seungyong L 2009 ACM Trans. Graphics(SIGGRAPH ASIA) 28 145

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