to retain the important textures of the original image.

### Multiframe Input

We first discuss the input of our proposed [**TRN**]{}. The conventional input for [**GAN**]{}s is a noise vector $z \in \mathbb{R}^{N}$ randomly generated according to the normal distribution. Then the noise vector $z$ is transformed into the desired output through the generator. Our network is similar to DeBlurGan [@kupyn2017deblurgan]. The architecture of DeBlurGan [@kupyn2017deblurgan] requires blurred image as an input and produce a deblurred image. Although blur is one of the consequences of turbulence observed in the frames, a single frame from a turbulence-distorted video as an input is experimentally shown to be ineffective in recovering the original image. Therefore, using the original architecture from DeBlurGan is insufficient to remove undesirable effects such as the geometric distortions. Motivated by this observation, the input in our network is a turbulence-distorted multiframe input originated from a clear image. Thus, the improved version of our new architecture is to include multiple frames as the input. Instead of taking the whole video sequence as the input, subsampled frames are selected.

Parameters: $M = 1000 $ - number of iterations $N = 32 $ - patch size $\mu$ - mean of the Guassian 