our input. The whole training process for 40 epochs takes around 3 days. Figure \[fig:train\] demonstrates that the restoration performance is gradually better in the training. The network firstly discards geometric distortion from the turbulence in the first few epochs and then attempts to deblur and preserve the texture of the original image in the remaining epochs.

![image]({train.png})

Result
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After the [**TRN**]{} is trained, we test its performance on more than 400 testing data consisting of simulated and real turbulence-distorted videos. The testing data are different from the training data. In this section, we report some of the experimental results.

Restoration of simulated turbulence-distorted videos
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Figure \[fig:build\_1\] and \[fig:build\_2\] show the restoration results of some simulated turbulence-distorted image sequences capturing different buildings. The first column shows the observed frames from each turbulence distorted image sequences, which are degraded by both geometric distortions and blurs. The middle column shows the restoration results using the [**TRN**]{} without the incorporation of the subsampling method. Note that most geometric distortions and blurs are removed, although some amount of distortions can still be observed. The right column shows the restoration results using the [**TRN**]{} with the incorporation of the subsampling method. With subsampling, 