用扩散模型从欠采样数据重建雷达图像，提升成像质量。
Diffusion Probabilistic Models for Compressive SAR Imaging
- 用扩散模型引导欠采样初始图像修复
- 真实雷达数据上实现更优成像质量
- 适合需要高效高精度成像的遥感应用
压缩感知合成孔径雷达（SAR）成像被建模为逆问题，传统方法依赖迭代优化，计算成本高昂。本文研究利用去噪扩散概率模型进行压缩SAR图像重建，以标准成像方法获得的欠采样数据作为初始重建结果，指导扩散模型生成高质量图像。在真实SAR数据上的实验表明，该方法在仅使用部分采样数据的情况下，成像质量优于采用完整数据集的标准重建方法，展示了其在提升成像性能方面的潜力。
原文摘要 · Abstract (English)
Compressed sensing Synthetic Aperture Radar (SAR) image formation, formulated as an inverse problem and solved with traditional iterative optimization methods can be very computationally expensive. We investigate the use of denoising diffusion probabilistic models for compressive SAR image reconstruction, where the diffusion model is guided by a poor initial reconstruction from sub-sampled data obtained via standard imaging methods. We present results on real SAR data and compare our compressively sampled diffusion model reconstruction with standard image reconstruction methods utilizing the full data set, demonstrating the potential performance gains in imaging quality.
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