Deep Learning-Enhanced Iterative Modified Contrast Source Method for Electromagnetic Imaging in Half-Space
學年 114
學期 1
出版（發表）日期 2025-11-19
作品名稱 Deep Learning-Enhanced Iterative Modified Contrast Source Method for Electromagnetic Imaging in Half-Space
作品名稱（其他語言）
著者 Wei-Tsong Lee ,Chien-Ching Chiu ,Po-Hsiang Chen ,Yen-Chun Li ,Hao Jiang
單位
出版者
著錄名稱、卷期、頁數 Mathematics, vol. 13, no. 22, a.n. 3711
摘要 This paper presents a hybrid inversion framework that integrates a physics-informed iterative algorithm with a deep learning-based refinement strategy to address the electromagnetic inverse scattering problem of a uniaxial object buried in lossy half-space environments. Specifically, an Iterative Modified Contrast Scheme (IMCS) is developed to accelerate convergence and produce stable initial estimates, yielding improved performance compared to conventional contrast source methods. These estimates are subsequently refined by U-Net architecture, thereby enhancing the image quality of the reconstructed dielectric targets. Numerical simulations demonstrate that the proposed framework achieves robust and high-fidelity reconstructions of buried high-contrast dielectric objects, even in the presence of 20% additive Gaussian noise.
關鍵字
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版