Adaptive optical image restoration method based on PSF reconstruction and improved Maximum A Posteriori estimation

The adaptive optics (AO) system can provide real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast and brings about a loss in quality. In this paper, a adaptive optical image restoration method was presented via the Point Spread Function (PSF) reconstruction and improved Maximum A Posteriori (MAP) estimation. Firstly, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect was developed. Secondly, the iterative solution formulas of AO image was build up based on the proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. A series of experiments on simulated degraded AO images were conducted to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm, Richardson-Lucy IBD algorithm and FS-MLJD algorithm, our algorithm has better restoration effects including higher Peak Signal-to-Noise Ratio (PSNR) and Laplacian Sum (LS) values than the others. For "Man" image, the PSNR has improved 6.83%, 4.47% and 2.28%, and the LS value has improved 22.2%, 7.9% and 12.1%, respectively. The research results have a certain application values for actual AO image restoration.