19250

Atmospheric turbulence removal using convolutional neural network

Jing Gao, N. Anantrasirichai, David Bull
Visual Information Laboratory, University of Bristol, UK
arXiv:1912.11350 [eess.IV], (22 Dec 2019)

@misc{gao2019atmospheric,

   title={Atmospheric turbulence removal using convolutional neural network},

   author={Jing Gao and N. Anantrasirichai and David Bull},

   year={2019},

   eprint={1912.11350},

   archivePrefix={arXiv},

   primaryClass={eess.IV}

}

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This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion. We have built an end-to-end supervised convolutional neural network (CNN) to reconstruct turbulence-corrupted video sequence. Our framework has been developed on the residual learning concept, where the spatio-temporal distortions are learnt and predicted. Our experiments demonstrate that the proposed method can deblur, remove ripple effect and enhance contrast of the video sequences simultaneously. Our model was trained and tested with both simulated and real distortions. Experimental results of the real distortions show that our method outperforms the existing ones by up to 3.8% in term of the quality of restored images, and it achieves faster speed than the state-of-the-art methods by up to 23 times with GPU implementation.
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