kernel $\sigma$ - standard deviation of the Guassian kernel $S$ - distortion strength, uniform from \[0.1,0.4\] $B$ - blur constant, uniform from \[0.1, 1\] Create a Gaussian from Normal CDF ($0.2 * \text{rand} - 1, \sigma$) $ x \gets \text{randi} (\text{width} -2 *N) + N$ $ y \gets \text{randi} (\text{height} -2 *N) + N$ $u(x-N:x+N,y-N:y+N) $ $\hspace{2cm}\gets u(x-N:x+N,y-N:y+N) + \text{randn} * S$ $v(x-N:x+N,y-N:y+N) $ $\hspace{2cm}\gets v(x-N:x+N,y-N:y+N) + \text{randn} * S$ Convolve the $u, v$ vector fields with the kernel Wrap the image with $u, v$ vector fields with the . Blur the image by convoluting it with a Gaussian smoothing window $w(n) = \exp(-\frac{n^2}{2 B^2})$\
Distorted Video Frames

With the data augmentation method described in last subsection, the training data is a multiple frames $I_{TD} = (I_{TD}^{(1)}, I_{TD}^{(2)}, ..., I_{TD}^{(n)} )$ transformed from the original clean image $I_C$ of size $r \times s$. In [**TRN**]{}, the input is a selected subsampled frames from $I_{TD}$. Instead of using the whole sequence of frames as the input, we randomly select $ m = 20$ frames from the whole video in the training stage as the input for the generator in the GAN model. In the testing stage, we incorporate a subsampling method 