images for training. It overcomes the problem of data scarcity. As such, the use of deep learning approaches to tackle the deturbulence problem is made possible that a sufficiently large dataset is not required.

3.  We propose to incorporate a subsampling method into the trained network to obtain a better restored image. Experimental results demonstrate that the performance of the proposed model can be significantly improved with the subsampling strategy.

Related Work
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Restoration of turbulence-distorted images
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The main tasks of restoring turbulence-corrupted images consist of the removal of geometric distortions and space-time varying blurs. It is in general challenging to discard both the geometric distortions and blurs simultaneously. Several previous works are firstly devoted to reconstruct a clean image through the process of image fusion by registering the image frames to a good reference image. Meinhardt-Llopis and Micheli [@meinhardt2014implementation] [@micheli2014linear] proposed a reference extraction method by registering frames to a ‘centroid’ image. The basic idea is to warp each image frame by the average deformation field between it and the other images from the turbulence-degraded video. This method has an assumption that the deformation between the original image and the distorted frames is zero on average. However, the 