title: Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.
abstract: An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.
authors.affiliation: School of Information Technology, Jilin Agricultural University, Changchun 130118, China. ldm0214@163.com.|School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China. ldm0214@163.com.|CSIRO Data61, PO Box 76, Epping, NSW 1710, Australia. ldm0214@163.com.
authors.forename: Dongming
authors.initials: D
authors.lastname: Li
authors.affiliation: CSIRO Data61, PO Box 76, Epping, NSW 1710, Australia. changming.sun@csiro.au.
authors.forename: Changming
authors.initials: C
authors.lastname: Sun
authors.affiliation: School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China. yangjh@cust.edu.cn.
authors.forename: Jinhua
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Information Technology, Jilin Agricultural University, Changchun 130118, China. huan_liu_2017@sohu.com.
authors.forename: Huan
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Information Technology, Jilin Agricultural University, Changchun 130118, China. 18404317389m@sina.cn.
authors.forename: Jiaqi
authors.initials: J
authors.lastname: Peng
authors.affiliation: CSIRO Data61, PO Box 76, Epping, NSW 1710, Australia. zhanglijuan@ccut.edu.cn.|College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China. zhanglijuan@ccut.edu.cn.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Zhang
keywords: adaptive optics; atmospheric turbulence; blind deconvolution; frame selection; image restoration; maximum likelihood