@inproceedings{16224007b4444460a3b61ce121c741ad,
title = "Research on improving the authenticity of simulated infrared image using adversarial networks",
abstract = "When the real infrared image is insufficient, the simulation infrared image is an important data supplement to the real infrared image. However, the authenticity of simulated infrared image often does not meet the requirements of real images. So improving the authenticity of simulated infrared image plays an important role in related fields. In order to achieve this goal, a method based on deep learning is proposed in this paper. Unlike traditional methods of using manual modification by experience, the proposed method can convert non-realistic simulation infrared image input into a realistic one with similar scene structure. First, we generate a large number of simulation infrared images through the simulation system. Then, we propose an optimization method to improve the authenticity of simulated infrared images. Finally, we designed a comparison experiment between the original simulation infrared image and the optimized simulation infrared image, and finally verify the effectiveness.",
keywords = "Generative adversarial network, Infrared image processing, Optimize method, Simulation infrared images",
author = "Xuejian Li and Chengpo Mu and Ruiheng Zhang and Yu Yang and Yanjie Wang",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 11th International Conference on Digital Image Processing, ICDIP 2019 ; Conference date: 10-05-2019 Through 13-05-2019",
year = "2019",
doi = "10.1117/12.2539602",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jenq-Neng Hwang and Xudong Jiang",
booktitle = "Eleventh International Conference on Digital Image Processing, ICDIP 2019",
address = "United States",
}