Simulating a turbulent video affected by spatiotemporally varying blur and tilt using temporal cross correlation of...

Videos captured in long-distance horizontal imaging through the atmosphere suffer from dynamic spatiotemporal movements and blur caused by the air turbulence. Simulations of atmospheric turbulence in such videos, which have been conducted in the past, are difficult to compute. Our goal in this research is to develop an effective simulation algorithm of videos affected by atmospheric turbulence characterized by spatiotemporally varying blur and tilt, when supplied with a given image. We accomplish this via extending an already established method that simulates atmospheric turbulence in a single image, by incorporating turbulence properties in the time domain that include both the tilts and blurring effects. This study also extends our previous work that simulated turbulence, but did not consider the space-varying property of the blur. This is done by employing the relationship between turbulence image distortions and the intermodal correlations of the Zernike coefficients in time and space, and also via analyzing the spatiotemporal matrix that represents the spatial correlation of movements between different frames. The proposed method can facilitate the production of simulations, given turbulence properties that include turbulence strength, object distance, and height. The simulation is applied to videos with low and high frame rates, and the differences between them are analyzed. The proposed method can prove useful when generating machine-learning algorithms that apply to videos affected by atmospheric turbulence, which require large labeled video datasets (with controlled turbulence and imaging parameters) for training.