Table of Contents Author Guidelines Submit a Manuscript
Abstract and Applied Analysis
Volume 2014, Article ID 781607, 10 pages
Research Article

Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

1School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China
2College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
3School of Information Technology, Jilin Agriculture University, Changchun 130118, China
4Informatization Center, Changchun University of Science and Technology, Changchun 130022, China
5College of Electrical and Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Received 18 April 2014; Revised 5 June 2014; Accepted 6 June 2014; Published 6 July 2014

Academic Editor: Fuding Xie

Copyright © 2014 Lijuan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.