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Image reconstruction of electromagnetic tomography based on generative adversarial network with spectral

Shuqing Jia¹, Ronghua Zhang¹, Wenying Fu¹

¹School of Artificial Intelligence, Tiangong University, Tianjin, China.

The Review of Scientific Instruments

|February 6, 2025

Summary

A new deep learning model, STDBOGAN, enhances electromagnetic tomography (EMT) image reconstruction by improving accuracy and reducing artifacts. This method offers better performance and robustness for industrial inspection applications.

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Area of Science:

* Electrical Engineering
* Computer Science
* Applied Mathematics

Background:

* Electromagnetic tomography (EMT) shows promise for industrial inspection.
* Current EMT image reconstruction is nonlinear and ill-posed, causing artifacts and quality issues.
* Existing methods lack robustness and detail accuracy.

Purpose of the Study:

* To develop an advanced deep learning model for improved EMT image reconstruction.
* To address the challenges of nonlinearity, ill-posedness, and artifacts in EMT imaging.
* To enhance the accuracy, quality, and robustness of reconstructed EMT images.

Main Methods:

* Proposed STDBOGAN: a generative adversarial network incorporating spectral normalization, two timescale update rule, and an improved dung beetle optimization algorithm.
* Spectral normalization and two timescale update rules stabilize training and mitigate gradient issues.
* Improved dung beetle optimization algorithm optimizes network hyperparameters for enhanced accuracy.

Main Results:

* STDBOGAN demonstrated superior performance, anti-noise capabilities, and generalization compared to UNet3+, DeepLabv3+, PSPNet, Segmenter, and SegRefiner.
* The model effectively reduced artifacts and improved image quality in simulations and physical experiments.
* Ablation studies confirmed the effectiveness of the proposed model improvements.

Conclusions:

* STDBOGAN significantly advances EMT image reconstruction quality.
* The model offers a robust solution for industrial inspection applications requiring high-fidelity imaging.
* This work contributes to overcoming the inherent limitations of traditional EMT reconstruction techniques.