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

Previous Articles     Next Articles

MRF reconstruction based on the markov network

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

  1. 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

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)

CLC Number: 

  • TP391

[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.

[1] WANG Xin-hua, OUYANG Ji-hong, ZHANG Guang, HE Yang. Super-resolution reconstruction of infrared images based on micro-scanner [J]. 吉林大学学报(工学版), 2017, 47(1): 235-241.
[2] BU Sha-sha, ZHANG Yu-jin. Single-frame and multi-frame image super-resolution based on locality-constrained linear coding [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 365-370.
[3] LIU Gang, ZHAO Hong-yi, HU Zhen-long. Weighted mixed-norm based blind super-resolution algorithm [J]. , 2012, 42(04): 1054-1058.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!