Abstract and Applied Analysis

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

Lijuan Zhang, Dongming Li, Wei Su, Jinhua Yang, and Yutong Jiang

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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.

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Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 781607, 10 pages.

First available in Project Euclid: 6 October 2014

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Zhang, Lijuan; Li, Dongming; Su, Wei; Yang, Jinhua; Jiang, Yutong. Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 781607, 10 pages. doi:10.1155/2014/781607. https://projecteuclid.org/euclid.aaa/1412606360

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