\(a) Observed          (b) [**TRN**]{} (no sub)         (c) [**TRN**]{} (with sub)
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![image](whole_1.png)

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   \(a) Observed          (b) [**TRN**]{} (no sub)         (c) [**TRN**]{} (with sub)
  --------------- --------------------------------- ----------------------------------

![image](whole_4.png)

To test the performance of our trained network to handle general large deformations, we randomly generate geometric distortions of an original image using large quasi-conformal deformations. More specifically, we randomly select some pixel positions in the image domain. A patch-wise triangular mesh is formed with the chosen position as the center. The method we use to generate artificial turbulence is deformation using Laplace-Beltrami solver (LBS) [@lam2014landmark]. We propose to assign the Beltrami coefficient $\mu$ which is a measure of nonconformality on each face vertex as follows: $$\mu = \bigg[(0.6 + \epsilon_1) \cos \Big((4 + \epsilon_2)\pi x^2 \Big) \bigg] + \bigg[(0.6 + \epsilon_3) \sin \Big((6 + \epsilon_4)\pi y^2\Big) \cos \Big((8 + \epsilon_5)\pi x y\Big) \bigg] i,$$ where $\epsilon_i$ are numbers randomly chosen in the range $[0,0.3]$ for $i=1,2$ and $[-1,1]$ for $i=3,4,5$. Then we obtain the deformation field by using the LBS solver, and wrap the image. By introducing image blurs to each deformed images, we obtain an image sequence with large geometric distortions and blurs. Note that the quasi-conformal 