基于Gauss-Markov随机场的贝叶斯盲复原

doi:10.11772/j.issn.1001-9081.2014.09.2708

计算机应用 ›› 2014, Vol. 34 ›› Issue (9): 2708-2710.DOI: 10.11772/j.issn.1001-9081.2014.09.2708

基于Gauss-Markov随机场的贝叶斯盲复原

周箩鱼,张正炳

长江大学电子信息学院，湖北荆州434023

收稿日期:2014-02-11 修回日期:2014-03-27 出版日期:2014-09-01 发布日期:2014-09-30
通讯作者: 周箩鱼
作者简介:
周箩鱼(1985-)，男，湖南邵阳人，讲师，博士研究生，主要研究方向：计算机图像处理、图像复原处理;
张正炳(1961-)，男，湖北随州人，教授，博士研究生，主要研究方向：图像与视频编码、多媒体通信。

Bayesian blind deblurring based on Gauss-Markov random field

ZHOU Luoyu,ZHANG Zhengbing

Electronics and Information School, Yangtze University, Jingzhou Hubei 434023, China

Received:2014-02-11 Revised:2014-03-27 Online:2014-09-01 Published:2014-09-30
Contact: ZHOU Luoyu

摘要/Abstract

摘要：

针对图像盲复原中图像细节恢复的同时块效应放大的问题，提出了一种贝叶斯盲复原算法。首先使用贝叶斯框架模式，对原始图像、观察图像、点扩散函数(PSF)及模型参数分别建立先验模型，并将能有效描述图像局部统计特征的带有高斯特性的Markov(Gauss-Markov)随机场模型作为原始图像的先验模型；然后利用贝叶斯公式推导出原始图像及点扩散函数的迭代公式。实验结果表明，与总变分(TV)先验模型的恢复图像相比，所提算法的恢复图像块效应明显减少，并且视觉效果更好；在点扩散函数的大小已知和未知的情况下，相比TV先验模型，所提算法的改善信噪比(ISNR)能提高1dB左右。

Abstract:

A Bayesian blind deblurring algorithm was proposed for solving the contradiction of image details restoration and blocking effect amplification. Based on Bayesian framework, prior models were established for origin image, observed image, point spread function and model parameters. Gauss-Markov random field model that can effectively describe local statistical features of image was introduced as prior model of origin image. Then the iterative formulas of origin image and the point spread function were deduced by using Bayesian formula. The experimental results show that image restorted by the proposed algorithm has fewer blocking effect and better visual effect than the restored image by Total Variation (TV) prior model. Whether the size of point spread function is known or not, compared with TV prior model, the proposed algorithm can increase the Improved Signal to Noise Ratio (ISNR) of the restored image about 1dB.

中图分类号:

TP391.413

周箩鱼张正炳. 基于Gauss-Markov随机场的贝叶斯盲复原[J]. 计算机应用, 2014, 34(9): 2708-2710.

ZHOU Luoyu ZHANG Zhengbing. Bayesian blind deblurring based on Gauss-Markov random field[J]. Journal of Computer Applications, 2014, 34(9): 2708-2710.

参考文献

［1］BISHOP T E, BABACAN S D, AMIZIC B, et al.Blind image deconvolution: problem formulation and existing approaches ［M］// CAMPISI P, EGIAZARIAN K. Blind image deconvolution: theory and applications. Boca Raton: CRC Press, 2007.

［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］WEN B, ZHANG Q, ZHANG J. Realization of iterative blind image restoration by self deconvolution and increment Wiener filter ［J］. Optics and Precision Engineering, 2011, 19(12): 3049-3055.(温博，张启衡，张建林.应用自解卷积和增量Wiener滤波实现迭代盲图像复原［J］.光学精密工程，2011,19(12):3049-3055.)

［4］MONEY J H, KANG S H. Total variation minimizing blind deconvolution with shock filter reference ［J］. Image and Vision Computing, 2008, 26(2): 302-314.

［5］LIAO H, NG M K. Blind deconvolution using generalized cross-validation approach to regularization parameter estimation ［J］. IEEE Transactions on Image Processing, 2011, 20(3): 670-680.

［6］LIKAS C L, GALATSANOS N P. A variational approach for Bayesian blind image deconvolution ［J］. IEEE Transactions on Signal Processing, 2004, 52(8): 2222-2233.

［7］CHANTAS G, GALATSANOS N, LIKAS A, et al.Variational Bayesian image restoration based on a product of t-distributions image prior ［J］. IEEE Transactions on Image Processing, 2008, 17(10): 1795-1805.

［8］BABACAN S D, MOLINA R, KATSAGGELOS A K. Variational Bayesian blind deconvolution using a total variation prior ［J］. IEEE Transactions on Image Processing, 2009, 18(1): 12-26.

［9］XIAO S, HAN G. Image restoration algorithm based on sparse regularized optimization ［J］. Journal of Computer Applications, 2012, 32(1): 261-263.(肖宿,韩国强.基于稀疏正则优化的图像复原算法［J］.计算机应用,2012,32(1):261-263.)

［10］OLIVEIRA J P, BIOUCAS-DIAS J M, FIGUEIREDO M A T. Adaptive total variation image deblurring: a majorization-minimization approach ［J］. Signal Processing, 2009, 89(9): 1683-1693.

［11］FARAMARZI E, RAJAN D, CHRISTENSEN M. A unified blind method for multi-image super-resolution and single/multi-image blur deconvolution ［J］. IEEE Transactions on Image Processing, 2013, 22(6): 2101-2114.

［12］BOX G E P, TIAO G C. Bayesian inference in statistical analysis ［M］. Hoboken: John Wiley and Sons, 2011.

［13］XU Z. Research on the practical technique of image blind restoration ［D］. Changsha: National University of Defense Technology, 2006: 45-49.(徐综琦.图像盲复原实用技术研究［D］.长沙：国防科学技术大学，2006:45-49.)

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基于Gauss-Markov随机场的贝叶斯盲复原

Bayesian blind deblurring based on Gauss-Markov random field

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