Super-resolution reconstruction method of the optical synthetic aperture image using generative adversarial network
Abstract: In order to solve the contradiction between large aperture elements and high-resolution images, in this study, we propose an improved image-resolution method based on generative adversarial network (GAN). First, we analyze the imaging principle of the optical synthetic aperture. Further, we improve a super-resolution GAN; especially, this network uses a multi-scale convolutional cascade to obtain global features of the image, and a multi-scale receptive field block and residual in residual dense block are built to obtain image details. In addition, this study uses the Mish function as the activation function of the discriminator to solve the problems of neuron extreme, gradient explosion, and poor generalization ability of the model. Through simulation, the results show that the proposed method can achieve a peak signal-to-noise ratio (PSNR) of 30 dB compared with traditional image super-resolution reconstruction methods for synthetic aperture image. The method proposed has an improvement of 2 dB in the PSNR and 0.016 in structure similarity index measure compared with the original super-resolution GAN. Therefore, this method can effectively reduce the image distortion and improve the quality of image reconstruction.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Super-resolution reconstruction method of the optical synthetic aperture image using generative adversarial network ; volume:22 ; number:1 ; year:2024 ; extent:14
Open physics ; 22, Heft 1 (2024) (gesamt 14)
- Creator
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Chen, Jing
Tian, Aileen
Chen, Ding
Guo, Meng
He, Dan
Liu, Yuwen
- DOI
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10.1515/phys-2023-0194
- URN
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urn:nbn:de:101:1-2024040515370625817841
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:54 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Chen, Jing
- Tian, Aileen
- Chen, Ding
- Guo, Meng
- He, Dan
- Liu, Yuwen
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