Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method
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 ada...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/781607 |
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| _version_ | 1849407561489448960 |
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| author | Lijuan Zhang Dongming Li Wei Su Jinhua Yang Yutong Jiang |
| author_facet | Lijuan Zhang Dongming Li Wei Su Jinhua Yang Yutong Jiang |
| author_sort | Lijuan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4891ea480a134f6fbde7500f87b85887 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-4891ea480a134f6fbde7500f87b858872025-08-20T03:36:01ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/781607781607Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization MethodLijuan Zhang0Dongming Li1Wei Su2Jinhua Yang3Yutong Jiang4School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Information Technology, Jilin Agriculture University, Changchun 130118, ChinaInformatization Center, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaTo 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.http://dx.doi.org/10.1155/2014/781607 |
| spellingShingle | Lijuan Zhang Dongming Li Wei Su Jinhua Yang Yutong Jiang Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method Abstract and Applied Analysis |
| title | Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method |
| title_full | Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method |
| title_fullStr | Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method |
| title_full_unstemmed | Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method |
| title_short | Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method |
| title_sort | research on adaptive optics image restoration algorithm by improved expectation maximization method |
| url | http://dx.doi.org/10.1155/2014/781607 |
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