Infrared imaging simulation of atmospheric turbulence based on a improved GAN network method

Xin Qu; Yuehuan Wang; Yifan Wu

doi:10.1117/12.2539244

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14 February 2020 Infrared imaging simulation of atmospheric turbulence based on a improved GAN network method

Xin Qu, Yuehuan Wang, Yifan Wu

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Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143015 (2020) https://doi.org/10.1117/12.2539244
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Infrared images of various complex environments and targets can be realistically generated by computer simulation technology. The infrared radiation generated by simulation is affected by many factors in the atmospheric transmission process, and atmospheric turbulence can significantly reduce the imaging quality (including image distortion, jitter, uneven illumination and blur). The traditional way to simulate the influence of atmospheric turbulence on images need to consider a variety of influencing factors, and the process is cumbersome. By improving the generation of the anti-network, the pixel gray loss function term is increased to reduce the infrared image distortion. The convergence of the GAN network is improved by increasing the GAN loss function with gradient constraints. Experiments show that the network obtained by the above method is stable and the generated image quality is high. In this paper, the structural similarity (SSIM) between the clear image and the aero-optical effect image corrected with the conditional generation adversarial the network is72.07%. The structural similarity (SSIM) between the original aero-optical effect image and the clear image is 57.02%.

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Xin Qu, Yuehuan Wang, and Yifan Wu "Infrared imaging simulation of atmospheric turbulence based on a improved GAN network method", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143015 (14 February 2020); https://doi.org/10.1117/12.2539244

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