Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866929531602862080 |
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| author | Sahlström, Teemu Tarvainen, Tanja |
| author_facet | Sahlström, Teemu Tarvainen, Tanja |
| contents | There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2204_06270 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography Sahlström, Teemu Tarvainen, Tanja Computational Physics Machine Learning 62F15, 68T07, 92C55 There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach. |
| title | Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography |
| topic | Computational Physics Machine Learning 62F15, 68T07, 92C55 |
| url | https://arxiv.org/abs/2204.06270 |