Super-resolution image restoration by maximum likelihood method and edge-oriented diffusion

Hao Zhu; Yu Lu; Qinzhang Wu

doi:10.1117/12.791021

19 February 2008 Super-resolution image restoration by maximum likelihood method and edge-oriented diffusion

Hao Zhu, Yu Lu, Qinzhang Wu

Proceedings Volume 6625, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications; 66250Y (2008) https://doi.org/10.1117/12.791021
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China

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.

Citation Download Citation

Hao Zhu, Yu Lu, and Qinzhang Wu "Super-resolution image restoration by maximum likelihood method and edge-oriented diffusion", Proc. SPIE 6625, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications, 66250Y (19 February 2008); https://doi.org/10.1117/12.791021

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