Photonic Sensors, 2018, 8 (1): 22, 网络出版: 2018-08-04  

Research on Adaptive Optics Image Restoration Algorithm Based on Improved Joint Maximum a Posteriori Method

Research on Adaptive Optics Image Restoration Algorithm Based on Improved Joint Maximum a Posteriori Method
作者单位
1 College of Computer Science and Engineering, Changchun University of Technology, Changchun, 130012, China
2 School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, 130117, China
摘要
Abstract
In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio (PSNR) and Laplacian sum (LS) value than the others. The research results have a certain application values for actual AO image restoration.

Lijuan ZHANG, Yang LI, Junnan WANG, Ying LIU. Research on Adaptive Optics Image Restoration Algorithm Based on Improved Joint Maximum a Posteriori Method[J]. Photonic Sensors, 2018, 8(1): 22. Lijuan ZHANG, Yang LI, Junnan WANG, Ying LIU. Research on Adaptive Optics Image Restoration Algorithm Based on Improved Joint Maximum a Posteriori Method[J]. Photonic Sensors, 2018, 8(1): 22.

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