the geometric distortions and blurs can be removed more successfully. The restoration results are more satisfactory compared to those without subsampling. It demonstrates the incorporation of the subsampling method into the deep network is beneficial.

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   \(a) Observed          (b) [**TRN**]{} (no sub)         (c) [**TRN**]{} (with sub)
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![image](whole_2.png)

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   \(a) Observed          (b) [**TRN**]{} (no sub)         (c) [**TRN**]{} (with sub)
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![image](whole_3.png)

We have test our deep network on some ’chimney’ image sequences. Figure \[fig:chimney\_1\] and \[fig:chimney\_2\] shows the restoration results of some simulated turbulence-distorted image sequences capturing different chimneys. Again, 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. The right column shows the restoration results using the [**TRN**]{} with the incorporation of the subsampling method. Again, with the incorporation of the subsampling method, the geometric distortions and blurs can be removed more successfully. The restoration results are more satisfactory compared to those without subsampling. It again demonstrates the benefit of incorporating the subsampling method into the deep network.

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