
Proceedings Paper
Super-resolution image restoration by maximum likelihood method and edge-oriented diffusionFormat | Member Price | Non-Member Price |
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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
Published in SPIE Proceedings Vol. 6625:
International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications
Liwei Zhou, Editor(s)
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
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Published in SPIE Proceedings Vol. 6625:
International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications
Liwei Zhou, Editor(s)
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