the unavailability of massive turbulence-distorted video frames disables the application of deep learning approaches to tackle the problem.

In this paper, we introduce a simple and yet effective data augmentation method to overcome the problem of data scarcity. The method models real turbulence with different deformations and different extent of blurs in order to provide sufficient training data. Since the artificial turbulence is randomly generated with different strength of deformations and blurs, a variety of turbulence-distorted videos can be produced from a single image. In general, it is known that the performance of image restoration is commensurate with the training sample size. Nevertheless, with the data augmentation method, the size requirement of the training data is not too restrictive and demanding in our proposed deep network to restore the turbulence-distorted images.

With the augmented training data, a deep network can be trained to solve the deturbulence problem. We propose a subsampled Wasserstein Generative Adversarial Network ([**WGAN**]{}) with multiframe input and $\ell_1$ cost to simultaneously remove geometric distortions and blurring effects of turbulence-distorted image sequences. [**WGAN**]{} is known for its effectiveness in generating a clear image from noises. Together with the $\ell_1$ cost applied to the network, important features of the 