the atmospheric turbulence is challenging due to the dramatic downgrade in the image quality from the geometric distortions and the space-time varying blurs. Multiple factors such as temperature changes, air turbulent flow, densities of air particles, carbon dioxide level and humidity lead to the occurrence of several turbulence layers with various changes in the refractive index [@hufnagel1964modulation] [@roggemann2018imaging]. These factors together explain the higher chance of obtaining corrupted video sequences in locations where the variation among these factors is large. In practice, either techniques in hardware-based adaptive optics [@pearson1976atmospheric] [@tyson2010principles] or methods in image processing [@shimizu2008super] [@seitz2009filter] [@li2007atmospheric] [@vorontsov1999parallel] [@hirsch2010efficient] are employed to remove the turbulence distortion in the images, but those prevailing models from either way can barely address to the majority of these factors.

Due to the fact that the atmospheric turbulence is complicated to be modeled, a deep learning approach which does not heavily require the underlying assumptions is more reasonable to tackle the problem than models relying on certain assumptions on the turbulence. We are thus motivated to investigate the possibility to remove geometric distortions and restore a good-quality image by using a generative model that does not explicitly take the above-mentioned factors into consideration. However, 