Learning Regularization Functionals for Inverse Problems: A Comparative Study
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909991314653184 |
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| author | Hertrich, Johannes Wong, Hok Shing Denker, Alexander Ducotterd, Stanislas Fang, Zhenghan Haltmeier, Markus Kereta, Željko Kobler, Erich Leong, Oscar Salehi, Mohammad Sadegh Schönlieb, Carola-Bibiane Schwab, Johannes Shumaylov, Zakhar Sulam, Jeremias Wache, German Shâma Zach, Martin Zhang, Yasi Ehrhardt, Matthias J. Neumayer, Sebastian |
| author_facet | Hertrich, Johannes Wong, Hok Shing Denker, Alexander Ducotterd, Stanislas Fang, Zhenghan Haltmeier, Markus Kereta, Željko Kobler, Erich Leong, Oscar Salehi, Mohammad Sadegh Schönlieb, Carola-Bibiane Schwab, Johannes Shumaylov, Zakhar Sulam, Jeremias Wache, German Shâma Zach, Martin Zhang, Yasi Ehrhardt, Matthias J. Neumayer, Sebastian |
| contents | In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insights. The proposed methods differ in their architectural design and training strategies, making direct comparison challenging due to non-modular implementations. We address this gap by collecting and unifying the available code into a common framework. This unified view allows us to systematically compare the approaches and highlight their strengths and limitations, providing valuable insights into their future potential. We also provide concise descriptions of each method, complemented by practical guidelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Regularization Functionals for Inverse Problems: A Comparative Study Hertrich, Johannes Wong, Hok Shing Denker, Alexander Ducotterd, Stanislas Fang, Zhenghan Haltmeier, Markus Kereta, Željko Kobler, Erich Leong, Oscar Salehi, Mohammad Sadegh Schönlieb, Carola-Bibiane Schwab, Johannes Shumaylov, Zakhar Sulam, Jeremias Wache, German Shâma Zach, Martin Zhang, Yasi Ehrhardt, Matthias J. Neumayer, Sebastian Machine Learning Numerical Analysis Optimization and Control In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insights. The proposed methods differ in their architectural design and training strategies, making direct comparison challenging due to non-modular implementations. We address this gap by collecting and unifying the available code into a common framework. This unified view allows us to systematically compare the approaches and highlight their strengths and limitations, providing valuable insights into their future potential. We also provide concise descriptions of each method, complemented by practical guidelines. |
| title | Learning Regularization Functionals for Inverse Problems: A Comparative Study |
| topic | Machine Learning Numerical Analysis Optimization and Control |
| url | https://arxiv.org/abs/2510.01755 |