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Proceedings Paper

Super-resolution image restoration by maximum likelihood method and edge-oriented diffusion
Author(s): Hao Zhu; Yu Lu; Qinzhang Wu
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Paper Abstract

We propose a super-resolution resolution algorithm on the basis of maximum likelihood (ML) method and edge-orient diffusion. By using Hammerseley-Clifford theorem, an image field assumed to be a Markov random field is Gibbs distributed. An edge-orient diffusion function is introduced and employed in the Gibbs prior. According to Bayesian theorem, the solution to the maximum likelihood function is equal to that to maximum a posterior function. Therefore we incorporate ML with a prior distributed function. Experimental results illustrate that our method has a powerful super-resolution restoration performance. Compared with traditional ML method, our approach can not only obtain super-resolution images, but also eliminate noise artifacts effectively without smoothing edges.

Paper Details

Date Published: 19 February 2008
PDF: 8 pages
Proc. SPIE 6625, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications, 66250Y (19 February 2008); doi: 10.1117/12.791021
Show Author Affiliations
Hao Zhu, Institute of Optics and Electronics (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Yu Lu, Institute of Optics and Electronics (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Qinzhang Wu, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 6625:
International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications

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