Volume 32 Issue 1
Jan.  2023
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YI Shi, LIU Xi, LI Li, et al., “Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 177-188, 2023, doi: 10.23919/cje.2021.00.084
Citation: YI Shi, LIU Xi, LI Li, et al., “Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 177-188, 2023, doi: 10.23919/cje.2021.00.084

Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network

doi: 10.23919/cje.2021.00.084
Funds:  This work was supported by the Open Foundation of Terahertz Science and Technology Key Laboratory of Sichuan Province (THZSC202001), Open Foundation of Key Laboratory of Industrial Internet of Things & Networked Control (2020FF06), and Sichuan Science and Technology Program (2020YFG0458)
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  • Author Bio:

    Shi YI was born in 1983. He received the M.S. degree from the University of Electronic Science and Technology of China in 2009. He is currently a Ph.D. student in the College of Mathematics and Physics, Chengdu University of Technology, Chengdu, China. He is currently an Associate Professor in College of Mechanical and Electrical Engineering, Chengdu University of Technology, China. His research interests include deep learning, infrared image processing, remote sensing image processing, and robot visual perception. (Email: 549745481@qq.com)

    Xi LIU was born in 1998. She received the B.E. degree from Mianyang Normal University, Mianyang, China, in 2021. She is currently an M.S. student at the College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China, under the supervision of Associate Professor Yi. Her research interests include deep learning, image processing, and signal processing. (Email: 1175812950@qq.com)

    Li LI was born in 1998. She received the B.E degree from Wenzheng College of Soochow University, Suzhou, China, in 2021. She is currently an M.S. student at the College of Mechanical and Electrical Engineering, Chengdu University of Technology, Sichuan, China, under the supervision of Associate Professor Yi. Her research interests include deep learning, image processing, and signal processing. (Email: 1024967126@qq.com)

    Xinghao CHENG was born in 1999. He received the B.E. degree in optoelectronic information science and engineering, Southwest Petroleum University, Nanchong, China, in 2021. He is currently an M.S. student at the College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China, under the supervision of Associate Professor Yi. His research interests include object detection, deep learning, image processing, and robot vision. (Email: 1115435123@qq.com)

    Cheng WANG was born in 1999. He received the B.E. degree from Faculty of Information Science and Technology, Chengdu University of Technology, Chengdu, China, in 2021. He is currently an M.S. student at the College of Mechanical and Electrical Engineering, Chengdu University of Technology, China, under the supervision of Associate Professor Yi. His research interests include deep learning, image processing, and algorithm migration of ROS. (Email: 1340499495@qq.com)

  • Received Date: 2021-03-02
  • Accepted Date: 2022-06-10
  • Available Online: 2022-07-02
  • Publish Date: 2023-01-05
  • The key to multi-sensor image fusion is the fusion of infrared and visible images. Fusion of infrared and visible images with generative adversarial network (GAN) has great advantages in automatic feature extraction and subjective vision improvement. Due to different principle between infrared and visible imaging, the blur phenomenon of edge and texture is caused in the fusion result of GAN. For this purpose, this paper conducts a novel generative adversarial network with blur suppression. Specifically, the generator uses the residual-in-residual dense block with switchable normalization layer as the elemental network block to retain the infrared intensity and the fused image textural details and avoid fusion artifacts. Furthermore, we design an anti-blur loss function based on Weber local descriptor. Finally, numerous experiments are performed qualitatively and quantitatively on public datasets. Results justify that the proposed method can be used to produce a fusion image with sharp edge and clear texture.
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