Project Description:
We present a deep-learning approach to restore a sequence of turbulence-distorted video frames from turbulent deformations and space-time varying blurs. Instead of requiring a massive training sample size in deep networks, we propose a training strategy that is based on a new data augmentation method to model turbulence from a relatively small dataset. Then we incorporate a subsampling method to enhance the restoration performance of the presented GAN model. The contributions of the paper are threefold: first, we introduce a simple but effective data augmentation algorithm to model the turbulence in real life for training in the deep network; Second, we propose the Wasserstein GAN combined with $\ell_1$ cost for successful restoration of turbulence-corrupted video sequence; Third, we combine a subsampling algorithm to filter out strongly corrupted frames to generate a video sequence with better quality.

Publication:

• W.H. Chak and L.M. Lui, Turbulence Removal Network, to be submitted (2018)