Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network

Shi YI; Zhijuan WU; Jingming ZHU; Xinrong LI; Xuesong YUAN

doi:10.11999/JEIT190495

Volume 42 Issue 7

Jul. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(7): 1766-1773

Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495

Citation:

Shi YI, Zhijuan WU, Jingming ZHU, Xinrong LI, Xuesong YUAN. Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1766-1773. doi: 10.11999/JEIT190495

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Motion Defocus Infrared Image Restoration Based on Multi Scale Generative Adversarial Network

doi: 10.11999/JEIT190495

1.
College of Information Science and Technology, Chengdu Universitly of Technology, Chengdu 610051, China
2.
College of Electronic Science and Engineering, University of Electronic Science and Technology, Chengdu 610054, China

Funds: The National Natural Science Foundation of China (61771096)

Received Date: 2019-07-03
Rev Recd Date: 2020-01-22

Available Online: 2020-03-25

Publish Date: 2020-07-23

Abstract

Abstract

Infrared thermal imaging system has obvious advantages in target recognition and detection at night, and the motion defocus blur caused by dynamic environment on mobile platform affects the application of the above imaging system. In order to solve the above problems, based on the research of infrared image restoration method after motion defocusing using generating confrontation network, a Infrared thermal image Multi scale deblurGenerative Adversarial Network (IMdeblurGAN) is proposed to suppress motion defocusing blurring effectively while preserving the image by using generating confrontation network to suppress the motion defocusing blurring of infrared image to hold the contrast of infrared image details, to improve the detection and recognition ability of night targets on motion platform. The experimental results show that compared with the existing optimal restoration methods for blurred images, Peak Signal to Noise Ratio (PSNR) of the image is increased by 5%, the Structure SIMilarity (SSIM) is increased by 4%, and the confidence score of YOLO for target recognition is increased by 6%.

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References(20)

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