摘要
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.
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.
基金
This research is supported by the State Scholarship Fund of China （No. 201508220093）, the National Science Foundation of China （No. 61402193）, the Scientific and Technological Research Project of the Department of Education in Jilin Province （No. JJKH20170575KJ, and No. 2014142）, and the Postdoctoral sustentation Fund of Jilin Province, the Department of Science and Technology of Jilin Province （No. 20160418080）.
