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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/5/370 |
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| Summary: | 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. |
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| ISSN: | 2078-2489 |