Year: 2024
Type: article
Abstract: This paper introduces a novel approach for reconstructing microwave imaging by combining the Whale Optimization Algorithm (WOA) with deep learning techniques. In it, electromagnetic waves are used to illuminate inhomogeneous dielectric objects in free space, and the scattered field is recorded. Due to the highly nonlinear nature of microwave imaging, the WOA is first employed to calculate an initial guess from the measured scattered field of dielectric objects. This step significantly reduces the training complexity for machine learning. Subsequently, the initial guess provided by the WOA is fed into a U-Net to accurately reconstruct the microwave image. Numerical simulation results indicate that the combination of the WOA and machine learning outperforms traditional methods under varying noise levels, enhancing the precision and effectiveness of the reconstruction process. In detail, the RMSE can be reduced 4–10% for dielectric constant distribution from 1 to 2.5 and SSIM can be increased about 30% for most cases.
Source: Electronics
Authors: Chien-Ching Chiu, Ching-Lieh Li, Po-Hsiang Chen, Hung-Ming Cheng, Hao Jiang
Institutions: Tamkang University, San Francisco State University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
OpenAlex ID: W4404059384