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Proceedings Paper

Restoration of turbulence-degraded images based on deep convolutional network
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Paper Abstract

Atmospheric turbulence is an irregular form of motion in the atmosphere. Because of turbulence interference, when the optical system through the atmosphere of the target imaging, the observed image will appear point intensity diffusion, image blur, image drift and other turbulence effects. Digital recovery of the turbulence-degraded images technique is a classical ill-conditioned problem by removing the blurring effect and suppressing the noise. Traditional approaches relying on image heuristics suffer from high frequency noise amplification and processing artifacts. In this paper, the image degradation models of the turbulent flow are given, the point spread function of turbulence is simulated by the similar Gaussian function model, and investigated a general framework of neural networks for restoring turbulence-degraded images. The blur and additive noise are considered simultaneously. Two solutions respectively exploiting fully convolutional networks (FCN) and conditional Generative Adversarial Networks (CGAN) are presented. The FCN based on minimizing the mean squared reconstruction error (MSE) in pixel space gets high PSNR. On the other side, the CGAN based on perceptual loss optimization criterion retrieves more textures. We conduct comparison experiments to demonstrate the performance at different degree of turbulence intensity from the training configuration. The results indicate that the proposed networks outperform traditional approaches for restoring high frequency details and suppressing noise effectively.

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390B (6 September 2019);
Show Author Affiliations
Xiangyu Bai, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Chuan He, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)
Xiaohua Liu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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