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Shuai Liang

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Proceedings Article | 24 January 2019 Paper

CGAN for simulation and digital image correction of aero transmission effect and aero heat radiation effect

Yi Bo Su, Zhong Yang Wang, Shuai Liang, Tian Xu Zhang

Proceedings Volume 11052, 110521M (2019) https://doi.org/10.1117/12.2523271

KEYWORDS: Radiation effects, Digital imaging, Image transmission, Imaging systems, Thermal effects, Geometrical optics, Signal to noise ratio, Computer programming, Aerodynamics, Gallium nitride

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The main influencing factor of imaging under high speed conditions is the aero-optical effect. Aero-optical effect is a kind of noise, which can be regarded as a superposition of three kinds of noises, system noise ，aero transmission effect and aero heat radiation effect. In this paper, we only consider aero transmission effect and aero heat radiation effect. The simulation and correction of aero-optical effects are important for terminal guidance. The aero-optical effect simulation method described in this paper uses a deep neural network (Conditional Generative Adversarial Networks). The use of big data allows the conditional generative adversarial networks to learn the mapping between clear pictures and pictures with aero-optical effects in training. Aero-optical effect correction is usually divided into two parts, the correction of the aero transmission effect and the correction of the aero heat radiation effect. In this paper, we use the conditional generative adversarial networks to train a large amount of data and learn the mapping relationship between the pictures with the aero-optical effects and the clear pictures in the training. And this mapping relationship is preserved in the form of bias and weights. It is not necessary to consider the aero-optical effect and the aero heat radiation effect separately. In this paper, the structural similarity (SSIM) between the real image and the simulated image generated by the conditional generation adversarial the network is 97.63%. The structural similarity (SSIM) between the clear image and the aero-optical effect image corrected with the conditional generation adversarial the network is 76.59%. The structural similarity (SSIM) between the original aero-optical effect image and the clear image is 55.73%.

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