Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network
This paper presents a novel method for the super-resolution reconstruction and generation of synthetic aperture radar (SAR) images with an improved single-image generative adversarial network (ISinGAN). Unlike traditional machine learning methods typically requiring large datasets, SinGAN needs only...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/5/370 |
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| author | Xuguang Yang Lixia Nie Yun Zhang Ling Zhang |
| author_facet | Xuguang Yang Lixia Nie Yun Zhang Ling Zhang |
| author_sort | Xuguang Yang |
| collection | DOAJ |
| description | This paper presents a novel method for the super-resolution reconstruction and generation of synthetic aperture radar (SAR) images with an improved single-image generative adversarial network (ISinGAN). Unlike traditional machine learning methods typically requiring large datasets, SinGAN needs only a single input image to extract internal structural details and generate high-quality samples. To improve this framework further, we introduced SinGAN with a self-attention module and incorporated noise specific to SAR images. These enhancements ensure that the generated images are more aligned with real-world SAR scenarios while also improving the robustness of the SinGAN framework. Experimental results demonstrate that ISinGAN significantly enhances SAR image resolution and target recognition performance. |
| format | Article |
| id | doaj-art-d3e8c2758fd44a23a38aec1d12ce6710 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-d3e8c2758fd44a23a38aec1d12ce67102025-08-20T03:47:58ZengMDPI AGInformation2078-24892025-04-0116537010.3390/info16050370Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial NetworkXuguang Yang0Lixia Nie1Yun Zhang2Ling Zhang3School of Mathematics and Information Engineering, Longdong University, Qingyang 745000, ChinaSchool of Mathematics and Information Engineering, Longdong University, Qingyang 745000, ChinaSchool of Electronic Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCollege of Engineering, Ocean University of China, Qingdao 266100, ChinaThis paper presents a novel method for the super-resolution reconstruction and generation of synthetic aperture radar (SAR) images with an improved single-image generative adversarial network (ISinGAN). Unlike traditional machine learning methods typically requiring large datasets, SinGAN needs only a single input image to extract internal structural details and generate high-quality samples. To improve this framework further, we introduced SinGAN with a self-attention module and incorporated noise specific to SAR images. These enhancements ensure that the generated images are more aligned with real-world SAR scenarios while also improving the robustness of the SinGAN framework. Experimental results demonstrate that ISinGAN significantly enhances SAR image resolution and target recognition performance.https://www.mdpi.com/2078-2489/16/5/370generative adversarial networkimage generationsuper resolution |
| spellingShingle | Xuguang Yang Lixia Nie Yun Zhang Ling Zhang Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network Information generative adversarial network image generation super resolution |
| title | Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network |
| title_full | Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network |
| title_fullStr | Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network |
| title_full_unstemmed | Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network |
| title_short | Image Generation and Super-Resolution Reconstruction of Synthetic Aperture Radar Images Based on an Improved Single-Image Generative Adversarial Network |
| title_sort | image generation and super resolution reconstruction of synthetic aperture radar images based on an improved single image generative adversarial network |
| topic | generative adversarial network image generation super resolution |
| url | https://www.mdpi.com/2078-2489/16/5/370 |
| work_keys_str_mv | AT xuguangyang imagegenerationandsuperresolutionreconstructionofsyntheticapertureradarimagesbasedonanimprovedsingleimagegenerativeadversarialnetwork AT lixianie imagegenerationandsuperresolutionreconstructionofsyntheticapertureradarimagesbasedonanimprovedsingleimagegenerativeadversarialnetwork AT yunzhang imagegenerationandsuperresolutionreconstructionofsyntheticapertureradarimagesbasedonanimprovedsingleimagegenerativeadversarialnetwork AT lingzhang imagegenerationandsuperresolutionreconstructionofsyntheticapertureradarimagesbasedonanimprovedsingleimagegenerativeadversarialnetwork |