扩散与展开的结合：基于交错学习校正的压缩SAR图像重建

中文摘要 AI 辅助

压缩合成孔径雷达（SAR）成像通常被建模为逆问题，并采用传统迭代优化方法求解，但这类方法计算成本可能非常高。我们研究了去噪扩散概率模型（DDPMs）在压缩SAR图像重建中的应用，其中扩散模型由通过标准成像方法从欠采样数据获得的较差初始重建结果引导。我们受传统压缩感知（CS）方法启发，用模型驱动的交错细化过程增强这种数据驱动方法，以提升SAR图像中的稀疏性和重尾特性。在真实ERS-1 SAR数据集上的实验结果表明，该方法相较于基线DDPM和现有最优方法具有持续改进，在重建保真度和结构相似性方面取得了显著提升，同时仅带来适度的计算开销。所提出的混合框架有效结合了扩散模型的表示能力和效率与基于模型优化的可解释性和鲁棒性，实现了精确的压缩SAR成像。

英文摘要

Compressive Synthetic Aperture Radar (SAR) imaging, typically formulated as an inverse problem and solved with traditional iterative optimisation methods, can be very computationally expensive. We investigate the use of denoising diffusion probabilistic models (DDPMs) 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 augment this data-driven method with model-driven interleaved refinement processes, inspired by traditional compressed sensing (CS) methods, to enhance sparsity and heavy tails in SAR images. Experimental results on real ERS-1 SAR datasets demonstrate consistent improvements over baseline DDPM and state-of-the-art methods, with notable gains in reconstruction fidelity and structural similarity, while incurring only modest computational overhead. The proposed hybrid framework effectively combines the representational power and efficiency of diffusion models with the interpretability and robustness of model-based optimisation, enabling accurate compressive SAR imaging.

发表机构

• University of Bristol（布里斯托大学）

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