发表机构
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学（KAUST））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出STO-PNP-CSI方案，将随机优化与即插即用正则化结合进对比源反演，降低计算成本并稳定重建，在合成和实验数据上验证了其高精度与鲁棒性。
AI 中文摘要
本文开发了一种将随机优化（STO）与即插即用（PNP）正则化集成到对比源反演（CSI）中的电磁反演方案，称为STO-PNP-CSI。标准CSI在每次迭代中求解每个发射器的对比源矢量，这在多发射器配置中代价高昂。STO则每次迭代仅求解一个随机选择的对比源矢量，这降低了每次迭代的计算成本，并有助于反演逃离较差的局部极小值和鞍点。然而，由此造成的信息损失增加了反演的不适定性。为应对这一问题，将Swin-Conv-UNet（SCUNet）去噪器作为隐式正则化器嵌入CSI方案中，提供了比传统手工设计正则化更强的学习先验，并稳定了重建过程。所提出的STO-PNP-CSI被应用于合成数据和实验数据。结果表明，与CSI相比，它在显著降低计算成本的情况下仍能获得准确的重建结果，包括在强非线性和测量噪声条件下。
英文摘要
An electromagnetic inversion scheme that integrates stochastic optimization (STO) and plug-and-play (PNP) regularization into contrast-source inversion (CSI), termed STO-PNP-CSI, is developed. Standard CSI solves for the contrast source vector of every transmitter at each iteration, which is expensive in a multi-transmitter configuration. STO instead solves for only one randomly selected contrast source vector per iteration, which reduces the per-iteration cost and can help the inversion escape poor local minima and saddle points. The resulting loss of information, however, increases the ill-posedness of the inversion. To counter this, the Swin-Conv-UNet (SCUNet) denoiser is plugged into the CSI scheme as an implicit regularizer, supplying a learned prior that is stronger than conventional hand-crafted ones and stabilizes the reconstruction. The proposed STO-PNP-CSI is applied to both synthetic and experimental data. The results show that it yields accurate reconstructions at substantially lower computational cost than CSI, including under strong nonlinearity and measurement noise.
