Research on atmospheric turbulence-degraded image restoration based on generative adversarial networks

Jiuming Cheng; Jianyu Li; Congming Dai; Yichong Ren; Gang Xu; Shuai Li; Xiaowei Chen; Wenyue Zhu

doi:10.1117/12.2681805

24 May 2023 Research on atmospheric turbulence-degraded image restoration based on generative adversarial networks

Jiuming Cheng, Jianyu Li, Congming Dai, Yichong Ren, Gang Xu, Shuai Li, Xiaowei Chen, Wenyue Zhu

Proceedings Volume 12706, First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023); 1270607 (2023) https://doi.org/10.1117/12.2681805
Event: First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023), 2023, Shanghai, China

Abstract

The imaging equipment working in the atmosphere will not only be limited by the performance of the imaging system, but also be affected by turbulence. In the fields of astronomical observation, ground-based remote sensing and remote monitoring, there is an urgent need for corresponding methods and technologies to eliminate the impact of atmospheric turbulence and obtain clear images. With the development of computer technology, atmospheric optics theory and image processing technology, more and more researchers hope to combine deep learning technology with atmospheric turbulence theory to reduce the impact of turbulence on imaging and obtain clear and stable images. In this paper, a turbulence image restoration technique based on Generative Adversarial Networks (GAN) is proposed, which is divided into generator network and discriminator network. The generator network is used to convert blurred images affected by turbulence into clear images. The discriminator network is used to compare the converted image with the real clear image to determine whether the image is real or generated. After the whole GAN is optimized and trained, the image transformed by the generator and the real and clear image cannot be distinguished from each other. Because the training of the GAN requires a large number of corresponding samples, it is difficult to obtain the images affected and unaffected by turbulence at the same time in real life, so this paper uses the statistical characteristics of turbulence to simulate a large number of images affected by turbulence. We used the trained GAN model to simulate turbulence image restoration and got some achievements.

Citation Download Citation

Jiuming Cheng, Jianyu Li, Congming Dai, Yichong Ren, Gang Xu, Shuai Li, Xiaowei Chen, and Wenyue Zhu "Research on atmospheric turbulence-degraded image restoration based on generative adversarial networks", Proc. SPIE 12706, First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023), 1270607 (24 May 2023); https://doi.org/10.1117/12.2681805

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KEYWORDS

Image restoration

Turbulence

Image processing

Atmospheric optics

Convolution

Image quality

Optical transfer functions

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