Abstract
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.
Highlights
Adaptive optics is developed in order to overcome the interference of atmospheric turbulence on the telescope imaging, and it can measure and correct the optical wavefront phase of atmospheric turbulence in realtime
In this paper, according to the characteristic of adaptive optics image, we establish a degradation image model for multiframe exposure image series, and the mathematical model of the point spread function (PSF) for adaptive optical system after closed loop correction is derived; the PSF model based on phase error and changing over time is solved, and we use the power spectral density of image and constrain support domain method for AO image denoising processing
This paper applied the actual AO imaging system parameters as prior knowledge combined with regularization technique to improve the EM blind deconvolution algorithm
Summary
Adaptive optics (abbreviated as AO) is developed in order to overcome the interference of atmospheric turbulence on the telescope imaging, and it can measure and correct the optical wavefront phase of atmospheric turbulence in realtime. When surface-to-air object imaging (e.g., astral target) is observed by ground-based optical telescopes, adaptive optics system can only realize part of the wavefront error correction, and the information of high frequency on target suppression and attenuation seriously [3, 4]. There are many image restoration algorithms to overcome the influence of atmospheric turbulence, such as iterative blind deconvolution algorithm (abbreviated as IBD) [4, 5], IBD based on Richardson-Lucy iteration (abbreviated as RL-IBD), IBD based on the Wiener algorithm (abbreviated as Wiener-IBD), and the algorithm based on the expectation maximization algorithm (abbreviated as EM) [6, 7]. This paper proposes a blind image restoration method by multiframe iteration with improved adaptive optical images based on expectation maximization algorithm.
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