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%.
Detecting underground target is important for national defense and security. Using the temperature field simulation, we can obtain the simulation model of the underground target. The data pattern of simulation is different from the data pattern of infrared remote sensing (RS), but the two patterns have a mapping relationship. We transform the data pattern of simulation to the data pattern of infrared RS, and then compare the transformed simulation data with the actual acquired infrared RS data to find the difference, so as to detect the underground target. Most of mappings of simulation data and infrared RS data have no sufficient robustness, and the mapping function is susceptible to external environmental factors. Using pix2pix model, a mapping approach is proposed to transform the simulation data to the infrared RS data. To evaluate this method, we take Deshengkou area of Beijing for experiment. Experiment shows that this mapping method has better robustness and adaptability.
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