volume | PIER Journals

Abstract

Electromagnetic inverse scattering (EIS) problem is challenging due to its properties of strong nonlinearity and ill-posedness, where existing deep learning approaches often lack systematic network refinement and comprehensive analysis of key factors affecting performance. This work introduces CAMO-Net, a U-Net-based framework for EIS that integrates a channel-attention mechanism and systematically optimizes architectural and training factors to address these limitations. By integrating channel attention into skip connections, adopting a multi-scale channel configuration, and fine-tuning key hyperparameters through controlled experiments, CAMO-Net achieves superior accuracy and robustness. Experimental results demonstrate that it reduces the mean relative error (MRE) by 32.5% and the mean squared error (MSE) by 34.1% compared to the baseline U-Net. Our results demonstrate that joint channel attention and multi-factor optimization provide an effective, reproducible pathway for high-precision EIS imaging, offering new insights for robust reconstruction in EIS problems.

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Dr. Tianhao Pan

Dr. Jianfa Liu