= 0.2$) [@xu2015empirical] and instance normalization layer [@ulyanovinstance]. U-Net is known to involve a contracting path for contextual preservation and a symmetric expanding path for localization, and hence was particularly successful in image segmentation, denoising and super-resolution. Thus, U-net is used as the main architecture for our generator network $G$.

The subsampled turbulence-distorted multiframe passes through 7 blocks of convolutional layers and 6 blocks of deconvolutional layers to generate a clear image. The first 7 blocks $B_C^{(1)}, B_C^{(2)}, ...,  B_C^{(7)}$ contains convolutional layers, followed by the 6 remaining blocks $B_D^{(1)}, B_D^{(2)}, ..., B_D^{(6)}$ consisting of deconvolutional layers. Each block $B_C^{(i)}$ contains convolutional layers, non-linear activation layers and instance normalization layers. The temporal features extracted in each block are down-sampled by max-polling except for the features of the last block $B_C^{(7)}$. The features in $B_C^{(6)}$ and $B_C^{(7)}$ are concatenated before passing through the block $B_D^{(1)}$ in order to retain the deep features without too much information loss. The feature collected in the first block $B_D^{(1)}$ is then concatenated with the feature from the block $B_C^{(5)}$ to output the feature in the second block $B_D^{(2)}$. Repeating the process, we obtain a clear image $I_C$ which is of the same size as the 