Turbulence video simulation using the spatiotemporal cross correlation of distortion fields

Abstract

Previous simulations of atmospheric turbulence in videos are computationally complex. The purpose of this study is to develop an efficient algorithm for simulating spatiotemporal videos affected by atmospheric turbulence, given a static image. We extend an existing method for the simulation of atmospheric turbulence in a single image by incorporating turbulence properties in the time domain and the blurring effect. We accomplish this through analysis of the correlation between turbulence image distortions in time and in space. The significance of this method is the ease with which it will be possible to produce a simulation, given properties of the turbulence (including turbulence strength, object distance, and height). We apply the simulation to low and high frame rate videos, and we show that the spatiotemporal cross correlation of the distortion fields in the simulated video matches the physical spatiotemporal cross correlation function. Such a simulation can be useful when developing algorithms that apply to videos degraded by atmospheric turbulence and require a large amount of imaging data for training.