自适应光学(AO)系统为大气湍流提供实时补偿,但AO图像的对比度通常很差, 这会导致图像质量下降。本文提出了一种基于点扩散函数(PSF)重构和改进的最大后验概率(MAP)估计的自适应光学图像恢复方法。首先, 结合观测条件和AO系统的特点, 建立基于波前相位信息的PSF重构模型; 其次, 基于本文提出的算法, 建立了AO图像的迭代求解公式, 实现多帧AO图像联合去卷积过程。为了验证本文算法的有效性, 对仿真的退化AO图像进行了一系列复原实验。实验结果表明,以“Man”图像为例, 与Wiener-IBD、RL-IBD及FS-MLJD算法相比, 该算法的峰值信噪比(PSNR)测度分别提高了683%, 4.47%和2.28%, 拉普拉斯梯度模(LS)值分别提高了22.2%, 17.9%和12.1%。本文的研究结果对实际的AO图像恢复具有一定的应用价值。

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.