Super-resolution image restoration by maximum likelihood method and edge-oriented diffusion
Hao Zhu, Yu Lu, Qinzhang Wu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.