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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14869 |
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| _version_ | 1866918400670826496 |
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| author | Costarelli, Danilo Piconi, Michele Troiani, Alessio |
| author_facet | Costarelli, Danilo Piconi, Michele Troiani, Alessio |
| contents | We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of parallel computing architecture in a natural and efficient manner. We compare the performance of the PCA method with the standard Gibbs sampler on an image denoising task in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The numerical results and the large speedups obtained with this approach suggest that PCA-based algorithms are a promising alternative for Bayesian inference in high-dimensional inverse problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising Costarelli, Danilo Piconi, Michele Troiani, Alessio Computation Probability We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of parallel computing architecture in a natural and efficient manner. We compare the performance of the PCA method with the standard Gibbs sampler on an image denoising task in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The numerical results and the large speedups obtained with this approach suggest that PCA-based algorithms are a promising alternative for Bayesian inference in high-dimensional inverse problems. |
| title | Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising |
| topic | Computation Probability |
| url | https://arxiv.org/abs/2507.14869 |