In this work, we introduce a learning-based method to achieve high-quality reconstructions for inverse scattering problems (ISPs). Particularly, the proposed method decouples the full-wave reconstruction model into two steps, including coarse imaging of dielectric profiles by the back-propagation scheme, and a resolution enhancement of coarse results as an image-to-image translation task solved by a novel perceptual generative adversarial network (PGAN). A perceptual adversarial (PA) loss, which is defined as a perceptual loss for the generator network using hidden layers from the discriminator network, is employed as a structural regularization in PGAN. The PA loss is further combined with the pixel-wise loss, and also possibly the adversarial loss, to enforce a multi-level match between the reconstructed image and its reference one. The adversarial training of the generator and discriminator networks ensures that the structural features of targets are dynamically learned by the generator. Numerical tests on both synthetic and experimental data verify that the proposed method is highly efficient and it achieves superior imaging results compared to other data-driven methods. The validation of the proposed PGAN on ISPs also provides a fast and high-precision way for solving other physics-related imaging problems.

中文翻译：

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具有感知生成对抗网络的电磁逆散射

在这项工作中，我们引入了一种基于学习的方法来实现逆散射问题 (ISP) 的高质量重建。特别地，所提出的方法将全波重建模型解耦为两个步骤，包括通过反向传播方案对电介质轮廓进行粗成像，以及通过新颖的感知方法解决的作为图像到图像转换任务的粗略结果的分辨率增强。生成对抗网络（PGAN）。感知对抗 (PA) 损失被定义为使用来自鉴别器网络的隐藏层的生成器网络的感知损失，被用作 PGAN 中的结构正则化。PA 损失进一步与逐像素损失相结合，也可能与对抗性损失相结合，以强制重建图像与其参考图像之间的多级匹配。生成器和判别器网络的对抗性训练确保生成器动态学习目标的结构特征。对合成数据和实验数据的数值测试验证了所提出的方法是高效的，并且与其他数据驱动的方法相比，它实现了卓越的成像结果。提议的 PGAN 在 ISP 上的验证也为解决其他与物理相关的成像问题提供了一种快速且高精度的方法。