Stability of Data-Dependent Ridge-Regularization for Inverse Problems
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909448273920000 |
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| author | Neumayer, Sebastian Altekrüger, Fabian |
| author_facet | Neumayer, Sebastian Altekrüger, Fabian |
| contents | Theoretical guarantees for the robust solution of inverse problems have important implications for applications. To achieve both guarantees and high reconstruction quality, we propose learning a pixel-based ridge regularizer with a data-dependent and spatially varying regularization strength. For this architecture, we establish the existence of solutions to the associated variational problem and the stability of its solution operator. Further, we prove that the reconstruction forms a maximum-a-posteriori approach. Simulations for biomedical imaging and material sciences demonstrate that the approach yields high-quality reconstructions even if only a small instance-specific training set is available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_12289 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stability of Data-Dependent Ridge-Regularization for Inverse Problems Neumayer, Sebastian Altekrüger, Fabian Optimization and Control Machine Learning Theoretical guarantees for the robust solution of inverse problems have important implications for applications. To achieve both guarantees and high reconstruction quality, we propose learning a pixel-based ridge regularizer with a data-dependent and spatially varying regularization strength. For this architecture, we establish the existence of solutions to the associated variational problem and the stability of its solution operator. Further, we prove that the reconstruction forms a maximum-a-posteriori approach. Simulations for biomedical imaging and material sciences demonstrate that the approach yields high-quality reconstructions even if only a small instance-specific training set is available. |
| title | Stability of Data-Dependent Ridge-Regularization for Inverse Problems |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2406.12289 |