DocumentCode :
2244517
Title :
Markov Chain Monte Carlo Super-resolution Image Reconstruction With Artifacts Suppression
Author :
Tian, Jing ; Ma, Kai-Kuang
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
fYear :
2006
fDate :
4-7 Dec. 2006
Firstpage :
940
Lastpage :
943
Abstract :
Recently, the Markov chain Monte Carlo (MCMC) technique has been proved as an effective approach to address the super-resolution image reconstruction problem. However, this approach usually requires a substantial amount of time to generate a sufficiently large number of samples for estimating the unknown high-resolution image. Limiting the simulation time could possibly lead to some artifacts presented in the reconstructed high-resolution image. To effectively mitigate these artifacts, an outlier-sensitive bilateral filtering is proposed in this paper, which contains a switching mechanism steered by an outliers detection scheme. Only for those pixel positions that have been identified containing outliers, our proposed bilateral filtering will be applied; for the rest, the conventional bilateral filtering will be exploited. Experimental results are presented to demonstrate the superior performance of the proposed method
Keywords :
Markov processes; Monte Carlo methods; image reconstruction; image resolution; Markov chain Monte Carlo super-resolution image reconstruction; artifacts suppression; outlier-sensitive bilateral filtering; Bayesian methods; Filtering; Fuses; Gaussian noise; Helium; Image reconstruction; Image resolution; Layout; Monte Carlo methods; Strontium; Markov chain Monte Carlo; super-resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 2006. APCCAS 2006. IEEE Asia Pacific Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0387-1
Type :
conf
DOI :
10.1109/APCCAS.2006.342216
Filename :
4145549
Link To Document :
بازگشت