Hybrid-Input Deep Learning Framework for Electromagnetic Imaging of Anisotropic Objects

This paper presents a hybrid-input deep learning framework for electromagnetic imaging of anisotropic objects in a two-dimensional environment. The proposed method combines transverse magnetic (TM) and transverse electric (TE) illuminations to reconstruct the directional permittivity components. Backpropagation (BP) and the direct sampling method (DSM) are first applied to the measured scattered fields to generate complementary preliminary images. These images are then superimposed to form a physically informed hybrid input for U-Net reconstruction. Numerical results show that the proposed hybrid-input strategy improves reconstruction accuracy, structural consistency, and noise robustness compared with single-input baselines using BP or DSM alone. The results demonstrate the potential of the proposed method for anisotropic electromagnetic imaging.