Whale Optimization Algorithm with Machine Learning for Microwave Imaging

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...

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Bibliographic Details
Published in:Electronics (Basel) 2024-11, Vol.13 (22), p.4342
Main Authors: Chiu, Chien-Ching, Li, Ching-Lieh, Chen, Po-Hsiang, Cheng, Hung-Ming, Jiang, Hao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Cetacea
Deep learning
Dielectric properties
Dielectrics
Efficiency
Electric properties
Electric waves
Electromagnetic radiation
Electromagnetic waves
Electromagnetism
Fourier transforms
Image enhancement
Image reconstruction
Machine learning
Mathematical optimization
Medical imaging equipment
Methods
Neural networks
Noise levels
Numerical analysis
Optimization
Optimization algorithms
Whales & whaling
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13224342