A Semi-Supervised Electromagnetic Imaging Algorithm Based, on Generative Adversarial Networks

摘要

In electromagnetic imaging applications, acquiring labeled data for supervised learning poses a significant challenge due to the high cost and time-consuming annotation processes. To address this limitation, we propose a semi-supervised electromagnetic imaging algorithm leveraging generative adversarial networks (GANs), which effectively integrates limited labeled data with abundant unlabeled measurements. Unlike conventional approaches that directly learn from raw scattered data, our method employs diffraction tomography (DT)-generated images as network inputs, thereby embedding spatial prior knowledge of scatterers to mitigate inherent artifacts such as boundary blurring and speckle noise. The framework features a modified U-Net architecture augmented with convolutional block attention modules (CBAMs) and residual blocks, enhancing feature extraction and segmentation robustness. Furthermore, adversarial training is introduced to refine the segmentation network using pseudo-labels generated from unlabeled DT images, enabling the discriminator to enforce physical consistency between labeled and unlabeled domains. Extensive simulations demonstrate the superiority of our method: when trained with only 100 labeled samples and 1,000 unlabeled samples, the proposed algorithm achieves a 23.0% reduction in mean squared error (MSE) compared to purely supervised counterparts. Additional validation on the handwritten digits and the “Austria” profile highlights its strong generalization capability for reconstructing unseen targets. This work bridges the gap between data-driven deep learning and physical priors, offering a practical solution for high-precision electromagnetic imaging under limited supervision.