Image Restoration Techniques Based On Bayesian Analysis

The presence image degradation is unavoidable due to the atmospheric turbulence, relative motion, defocus, imaging device limitations, noise and other factors. However, in many applications, the high-definition and quality images are needed。The image restora-tion technique is to restore original image for degraded image and is significant. It is the fundamental problem of image processing, pattern recognition and machine vision and is widely used in astronomy, remote sensing, medical image and military, etc.This dissertation focuses on the research of image restoration, including image prior models, the Bayesian inference for image estimation and NAS-RIF algorithms. Firstly, we research on Gauss random field models, and proposed EM algorithms for image restora-tion based on Katsaggelos(1991)'s research. Considering the computational complexity of Bayesian image restoration, image restoration is divided into two parts, denoising and deblurring. What's more, hierarchical Markov random field model was proposed for de-noising based on the Besag's research on spatially distributed data and Molina's research on Bayesian image restoration. In the research of denoising, EM algorithm and MCMC algorithm was given by the paper.Finally, we get the restored image with help of NAS-RIF algorithm.