光场相机在湍流下的清晰成像和点云计算

Abstract

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.