Diff-INR: Generative Regularization for Electrical Impedance Tomography
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909310573871104 |
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| author | Tong, Bowen Wang, Junwu Liu, Dong |
| author_facet | Tong, Bowen Wang, Junwu Liu, Dong |
| contents | Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs conductivity distributions within a body from boundary measurements. However, EIT reconstruction is hindered by its ill-posed nonlinear inverse problem, which complicates accurate results. To tackle this, we propose Diff-INR, a novel method that combines generative regularization with Implicit Neural Representations (INR) through a diffusion model. Diff-INR introduces geometric priors to guide the reconstruction, effectively addressing the shortcomings of traditional regularization methods. By integrating a pre-trained diffusion regularizer with INR, our approach achieves state-of-the-art reconstruction accuracy in both simulation and experimental data. The method demonstrates robust performance across various mesh densities and hyperparameter settings, highlighting its flexibility and efficiency. This advancement represents a significant improvement in managing the ill-posed nature of EIT. Furthermore, the method's principles are applicable to other imaging modalities facing similar challenges with ill-posed inverse problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04494 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diff-INR: Generative Regularization for Electrical Impedance Tomography Tong, Bowen Wang, Junwu Liu, Dong Image and Video Processing Computer Vision and Pattern Recognition Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs conductivity distributions within a body from boundary measurements. However, EIT reconstruction is hindered by its ill-posed nonlinear inverse problem, which complicates accurate results. To tackle this, we propose Diff-INR, a novel method that combines generative regularization with Implicit Neural Representations (INR) through a diffusion model. Diff-INR introduces geometric priors to guide the reconstruction, effectively addressing the shortcomings of traditional regularization methods. By integrating a pre-trained diffusion regularizer with INR, our approach achieves state-of-the-art reconstruction accuracy in both simulation and experimental data. The method demonstrates robust performance across various mesh densities and hyperparameter settings, highlighting its flexibility and efficiency. This advancement represents a significant improvement in managing the ill-posed nature of EIT. Furthermore, the method's principles are applicable to other imaging modalities facing similar challenges with ill-posed inverse problems. |
| title | Diff-INR: Generative Regularization for Electrical Impedance Tomography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.04494 |