Enhancing SAR Ship Image Generation with Context-Guided Upsampling in DDGAN
Abstract
Deep learning-based synthetic aperture radar (SAR) ship image processing is valuable for civilian and military applications, yet training deep models requires abundant high-resolution SAR ship images. Due to the high cost of SAR sensors and limited data availability, constructing large-scale, high-quality datasets remains challenging. SAR image generation offers a promising solution. However, most existing generation methods focus on natural images, with few targeting SAR ship targets. This paper proposes a high-resolution SAR ship image generation method using an improved Denoising Diffusion Generative Adversarial Network (DDGAN). To enhance detail generation, a Context-Aware Upsampling Filter (CAUF) is designed to incorporate SAR-specific information, enabling the backbone network to capture more effective features during upsampling. Experiments show that our method achieves a Fréchet Inception Distance (FID) of 55.21 and a Structural Similarity Index (SSIM) of 0.7245, producing high-quality SAR-consistent images with rich details.
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