Journal of System Simulation
Abstract
Abstract: Aiming at the low resolution of infrared images, an improved SRGAN super-resolution reconstruction algorithm is designed. In the generative network, the method of applying the residual dense network to obtain the image features extracted from each network layer so as to retain more high-frequency information of the image, and adopting a progressive upsampling method to improve the super-resolution reconstruction effect under a large scaling factor. In terms of the loss function, the perceptual loss that is more in line with human senses is adopted to make the generated image being closer to the real high-resolution image of senses and content. Experimental results show that the quality of reconstructed infrared image is better than that of the current representative methods in the subjective and objective evaluation.
Recommended Citation
Lei, Hu; Wang, Zugen; Tian, Chen; and Zhang, Yongmei
(2021)
"An Improved SRGAN Infrared Image Super-Resolution Reconstruction Algorithm,"
Journal of System Simulation: Vol. 33:
Iss.
9, Article 12.
DOI: 10.16182/j.issn1004731x.joss.20-0450
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss9/12
First Page
2109
Revised Date
2020-09-23
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0450
Last Page
2118
CLC
TP391.4
Recommended Citation
Hu Lei, Wang Zugen, Chen Tian, Zhang Yongmei. An Improved SRGAN Infrared Image Super-Resolution Reconstruction Algorithm[J]. Journal of System Simulation, 2021, 33(9): 2109-2118.
DOI
10.16182/j.issn1004731x.joss.20-0450
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