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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.00762 |
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| _version_ | 1866917942784950272 |
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| author | Shi, Fangming Liu, Jinzhen Meng, Xiangqian Zhou, Yapeng Xiong, Hui |
| author_facet | Shi, Fangming Liu, Jinzhen Meng, Xiangqian Zhou, Yapeng Xiong, Hui |
| contents | This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT), referred to as MR-EIT, which is capable of operating in both supervised and unsupervised learning modes. MR-EIT integrates an ordered feature extraction module and an unordered coordinate feature expression module. The former achieves the mapping from voltage to two-dimensional conductivity features through pre-training, while the latter realizes multi-resolution reconstruction independent of the order and size of the input sequence by utilizing symmetric functions and local feature extraction mechanisms. In the data-driven mode, MR-EIT reconstructs high-resolution images from low-resolution data of finite element meshes through two stages of pre-training and joint training, and demonstrates excellent performance in simulation experiments. In the unsupervised learning mode, MR-EIT does not require pre-training data and performs iterative optimization solely based on measured voltages to rapidly achieve image reconstruction from low to high resolution. It shows robustness to noise and efficient super-resolution reconstruction capabilities in both simulation and real water tank experiments. Experimental results indicate that MR-EIT outperforms the comparison methods in terms of Structural Similarity (SSIM) and Relative Image Error (RIE), especially in the unsupervised learning mode, where it can significantly reduce the number of iterations and improve image reconstruction quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks Shi, Fangming Liu, Jinzhen Meng, Xiangqian Zhou, Yapeng Xiong, Hui Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing This paper presents a multi-resolution reconstruction method for Electrical Impedance Tomography (EIT), referred to as MR-EIT, which is capable of operating in both supervised and unsupervised learning modes. MR-EIT integrates an ordered feature extraction module and an unordered coordinate feature expression module. The former achieves the mapping from voltage to two-dimensional conductivity features through pre-training, while the latter realizes multi-resolution reconstruction independent of the order and size of the input sequence by utilizing symmetric functions and local feature extraction mechanisms. In the data-driven mode, MR-EIT reconstructs high-resolution images from low-resolution data of finite element meshes through two stages of pre-training and joint training, and demonstrates excellent performance in simulation experiments. In the unsupervised learning mode, MR-EIT does not require pre-training data and performs iterative optimization solely based on measured voltages to rapidly achieve image reconstruction from low to high resolution. It shows robustness to noise and efficient super-resolution reconstruction capabilities in both simulation and real water tank experiments. Experimental results indicate that MR-EIT outperforms the comparison methods in terms of Structural Similarity (SSIM) and Relative Image Error (RIE), especially in the unsupervised learning mode, where it can significantly reduce the number of iterations and improve image reconstruction quality. |
| title | MR-EIT: Multi-Resolution Reconstruction for Electrical Impedance Tomography via Data-Driven and Unsupervised Dual-Mode Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2503.00762 |