Yang Huizhen, Li Xinyang, Gong Chenglong, Jiang Wenhan
The Key Lab on Adaptive Optics, Chinese Academy of Sciences, P. O. Box 350, Chengdu 610209, China.
Opt Express. 2009 Mar 2;17(5):3052-62. doi: 10.1364/oe.17.003052.
An adaptive optics (AO) system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object. SPGD is used to search the optimum voltages for the actuators of the deformable mirror. We try to find a convenient image performance metric, which is needed by SPGD, merely from a gray level distorted image and without any additional optics elements. Simulation results show the gray level variance function acts more promising than other metrics, such as metrics based on the gray level gradient of each pixel. The restoration capability of the AO system is investigated with different images and different turbulence strength wave-front aberrations using SPGD with the above resultant image quality criterion. Numerical simulation results verify the performance metric is effective and the AO system can restore those images degraded by different turbulence strengths successfully.
对一个采用随机并行梯度下降（SPGD）算法和61单元变形镜的自适应光学（AO）系统进行了模拟，以恢复被湍流退化的扩展目标的图像。SPGD用于搜索变形镜致动器的最佳电压。我们试图仅从灰度失真图像中找到一种方便的图像性能指标，该指标是SPGD所需要的，且无需任何额外的光学元件。模拟结果表明，灰度方差函数比其他指标（如基于每个像素灰度梯度的指标）更具前景。利用上述所得图像质量标准，采用SPGD对不同图像和不同湍流强度波前像差下AO系统的恢复能力进行了研究。数值模拟结果验证了该性能指标是有效的，且AO系统能够成功恢复被不同湍流强度退化的图像。
