  This paper describes a novel deep learning-based method for mitigating the
effects of atmospheric distortion. We have built an end-to-end supervised
convolutional neural network (CNN) to reconstruct turbulence-corrupted video
sequence. Our framework has been developed on the residual learning concept,
where the spatio-temporal distortions are learnt and predicted. Our experiments
demonstrate that the proposed method can deblur, remove ripple effect and
enhance contrast of the video sequences simultaneously. Our model was trained
and tested with both simulated and real distortions. Experimental results of
the real distortions show that our method outperforms the existing ones by up
to 3.8% in term of the quality of restored images, and it achieves faster speed
than the state-of-the-art methods by up to 23 times with GPU implementation.
