Inverse Reconstruction of Uniaxial Dielectric Objects in Slab Medium Using Deep Learning Techniques

Electromagnetic imaging in a slab medium presents significant challenges due to complex wave reflections and refractions at the interfaces of different layers. Multiple scattering and interference increase ill-posedness and nonlinearity, degrading reconstruction accuracy and stability. Under transverse magnetic (TM) and transverse electric (TE) excitations, we compare the CNN-refined reconstructions based on the Back Propagation Scheme (BPS) and the Dominant Current Scheme (DCS) to solve the Electromagnetic Inverse Scattering (EMIS) problem. Numerical results demonstrate that our proposed method can accurately reconstruct buried objects of various sizes and positions, even in the presence of noise. In particular, the DCS-CNN framework yields superior reconstruction performance compared to the BPS-CNN approach, highlighting the advantage of integrating the DCS with DL for imaging in a slab medium. Overall, this work validates the feasibility and effectiveness of combining preliminary imaging with DL, offering practical potential for solving complex inverse scattering problems.