无变形镜条件下抵抗大气湍流影响获取几百米至几十公里外目标的清晰成像和点云数据具有重要意义。光场相机是清晰成像和点云计算领域的有力工具，但是它在湍流条件下无法正常工作。同时光场相机技术的主要研究方向集中在如何提高点云的精度和密度，暂无人将其应用于湍流清晰成像。基于相空间光学原理改进了光场相机的信息提取算法，在湍流条件下完成了清晰成像和点云计算。这种算法使用四维密度函数来描述复眼结构，对原始数据的使用更加充分，能够提取物点完整的低阶相位信息，因而，可以抵抗湍流对局部子孔径的影响，稳健地获取目标点云，解算深度图并得到全聚焦清晰成像。根据这一原理设计了光场相机系统，探测室内湍流池后方的目标以及室外湍流下500 m处目标，均获得了4 k以上个物点的准确低阶相位分布，给出了目标的三维点云图和清晰成像。结果表明，该方法无需变形镜系统，也不需要先验信息，是一种稳定工作的解析算法。

It is of great significance to obtain clear imaging and point cloud data of targets from hundreds to dozens of kilometers away under atmospheric turbulence without deformable mirror. Light field cameras are powerful tools in the field of image clarification and point cloud calculation, but they don't work well in turbulent conditions. Meanwhile, the main research direction of light field camera technology focuses on how to improve the precision and density of point cloud, and no one applies it to turbulence image clarification temporarily. This job was finished by improving information extraction algorithm of light field camera based on phase space optics. This algorithm was more fully to use RAW data, because of adopting four dimensional density functions to describe the structure of compound eye, and therefore, it could resist the influence of turbulence on local sub-aperture images, acquire target point cloud steady, calculate the depth map and clarify turbulence-degraded image. Light field camera based on such method acquired more than 4 k accurate wavefront distribution, when it was used for detecting indoor target behind the turbulence pool and outdoor target 500 m far from the camera, and 3D point clouds and clear image were obtained successfully. The results show that this method is a stable analytical algorithm without deforming mirror system or prior information.