images can be restored even though they are corrupted. To gather enough information, it is natural to take multiple frames from the video as the input of the turbulence-removal network. Using multiple frames as input is essential to obtain a clear image from a turbulence-distorted video.

In the testing stage, we propose to incorporate a subsampling algorithm to the trained network for better performance. Usually, turbulence-distorted video consists of mildly distorted frames. The subsampling method extracts those sharp and mildly distorted frames in order to achieve an even better restoration result. We experimentally show that by incorporating the subsampling method, the performance of removing geometric distortions and blurs of the degraded images can be significantly improved.

Contributions
-------------

The main contributions of this paper are listed as follows:

1.  We propose a deep-learning approach, which is a [**WGAN**]{} model with the multiframe input and $\ell_1$ loss, for the restoration of turbulence-distorted images. To the best of our knowledge, it is the first work to study the feasibility of applying deep convolutional neural network for solving the deturbulence problem to simultaneously remove geometric distortions and space-time varying blurs.

2.  We propose a data augmentation method to generate geometrically distorted and blurry 