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Research on improving the authenticity of simulated infrared image using adversarial networks
Author(s): Xuejian Li; Chengpo Mu; Ruiheng Zhang; Yu Yang; Yanjie Wang
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Paper 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.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117902 (14 August 2019); doi: 10.1117/12.2539602
Show Author Affiliations
Xuejian Li, Beijing Institute Of Technology (China)
Chengpo Mu, Beijing Institute Of Technology (China)
Ruiheng Zhang, Beijing Institute Of Technology (China)
Yu Yang, Beijing Institute of Technology (China)
Yanjie Wang, Beijing Institute Of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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