基于学习的马尔科夫超分辨率复原

吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 406-409.

基于学习的马尔科夫超分辨率复原

薛翠红¹, 于明¹, 杨宇皓², 阎刚¹, 贾超¹

1. 河北工业大学计算机科学与软件学院,天津 300401;
2. 河北工业大学信息工程学院,天津 300401

收稿日期:2012-06-19 发布日期:2013-06-01
通讯作者: 于明(1964-),男,教授,博士生导师.研究方向:图像处理.E-mail:yueeming@126.com E-mail:yueeming@126.com
作者简介:薛翠红(1980-),女,讲师,博士研究生.研究方向:图像处理和图像超分辨率.E-mail:xuecuihong@scse.hebut.edu.cn
基金资助:
河北省科技支撑计划项目(11213518D);河北省高等学校科学技术研究项目(Z2011293).

MRF reconstruction based on the markov network

XUE Cui-hong¹, YU Ming¹, YANG Yu-hao², YAN Gang¹, JIA Chao¹

1. School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China;
2. School of Information Engineering, Hebei University of Technology, Tianjin 300401, China

Received:2012-06-19 Published:2013-06-01

摘要/Abstract

摘要：

针对最大后验概率(MAP)算法对图像边缘和细节保持能力不强问题,在传统马尔科夫模型算法的基础上,提出将MAP算法与凸集投影法(POCS)相融合的算法,将MAP算子当作凸约束集引入POCS算法里,这样可继承两者的优点。实验验证了算法的有效性,与传统学习算法相比,具有更好的图像复原效果。

关键词: 马尔科夫网络, 超分辨率, 贝叶斯理论, 凸集投影法, 最大后验概率

Abstract:

According to the problem of keeping image border and details weakly by MAP algorithm, a new method which mixed MAP and POCS for image super resolution based on Markov Network is introduced.The MAP operator is introduced to the POCS algorithm as a convex set of constraints.The POCS algorithm can be a good complementary to the MAP algorithm to overcome their original defect.Experiment shows that the proposed algorithm has better image restoration effect than the traditional algorithm.

Key words: Markov network, super-resolution, Bayesian theory, project onto convex sets(POCS), maximum a posteriori probability(MAP)

中图分类号:

TP391

薛翠红, 于明, 杨宇皓, 阎刚, 贾超. 基于学习的马尔科夫超分辨率复原[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 406-409.

XUE Cui-hong, YU Ming, YANG Yu-hao, YAN Gang, JIA Chao. MRF reconstruction based on the markov network[J]. 吉林大学学报(工学版), 2013, 43(增刊1): 406-409.

参考文献

[1] RAJAN D,CHAUDHURI S.Generalized interpolation and its application in super-resolution imaging[J].Image and Vision Computing,2001,19(13):957-969.

[2] Nguyen N,Milanfar P.A computationally efficient superresolution image reconstruction algorithm[J].IEEE Trans on Image Processing,2001,4 (8):573-583.

[3] Chantas G K,Galat sanos N P,Woods N A.Super-resolution based on fast registration and maximum a posteriori reconstruction[J].IEEE Trans on Image Processing,2007,16 (7):1821-1829.

[4] Ogawa T,Haseyama M. Semantic image retrieval based on POCS algorithm using kernel PCA and its performance verification [C]//ISCE'09,IEEE 13th Internatioinal Symposium on Consumer Electronics,Kyoto,2009:582-583.

[5] Zhang Fan,Zhu Qi-dan.Super-resolution image reconstruction for omni-vision based on POCS [C]//CCDC'09,Proceedings of the 21st Annual International Conference on Chinese Control and Decision Conference,Guilin:IEEE Press,2009:5045-5049.

[6] ROBINSON D,MILANFAR P.Fundamental performance limits in image registration[J].IEEE Trans Image Processing,2004,13(6):1185-1199.

[7] LEE E,KANG M G.Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration[J].IEEE Trans Image Processing,2003,12(7):826-837.

[8] Candocia F M,Principe J C.Super-resolution of images based on local correlations[J].IEEE Trans on Neural Networks,1999,10(2):372-380.

[9] Freeman W T,JONES T R,PASZTOR E C.Example based super-resolution[J].IEEE Computer Graphics and Applications,2002,22(2):56-65.

[10] BISHOP C M,BLAKE A,MARTH I B.Super-resolution enhancement of video [C]//International Conference on Artificial Intelligence and Statistics,Key West,USA,2003:410-414.

[11] Su Bing-hua,Jin Wei-qi.POCS-MPMAP based super-resolution image restoration[J].Acta Photonica Sinca,2003,32(4):502-504.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed