---
abstract: 'We present a deep-learning approach to restore a sequence of turbulence-distorted images 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 data augmentation method to model the turbulence from a relatively small dataset. Then we incorporate a subsampling method into the deep network to enhance the restoration performance of the trained [**GAN**]{} model. The contribution of the paper is threefold: First, we introduce a simple but effective data augmentation algorithm to model the turbulence for training in the deep network; Second, we propose the [**W**]{}asserstein [**GAN**]{} combined with the multiframe input and $\ell_1$ cost for successful restoration of turbulence-corrupted video sequence; Third, we incorporate a subsampling algorithm into the deep network to filter out strongly corrupted frames so as to obtain an improved restored image.'
address: 'Department of Mathematics, The Chinese University of Hong Kong, Hong Kong'
author:
- Wai Ho Chak
- Chun Pong Lau
- 'Lok Ming Lui,'
bibliography:
- 'mybibfile.bib'
title: Subsampled Turbulence Removal Network
---

turbulence ,data augmentation ,subsamping ,deep learning ,[**WGAN**]{}

Introduction
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The problem of image restoration from a sequence of frames under 