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arXiv · 2510.01755

Learning Regularization Functionals for Inverse Problems: A Comparative Study

Johannes Hertrich·Hok Shing Wong·Alexander Denker·Stanislas Ducotterd·Zhenghan Fang·Markus Haltmeier·Željko Kereta·Erich Kobler·Oscar Leong·Mohammad Sadegh Salehi·Carola-Bibiane Schönlieb·Johannes Schwab·Zakhar Shumaylov·Jeremias Sulam·German Shâma Wache·Martin Zach·Yasi Zhang·Matthias J. Ehrhardt·Sebastian Neumayer
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Abstract

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.

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Johannes Hertrich, Hok Shing Wong, Alexander Denker, Stanislas Ducotterd, Zhenghan Fang, Markus Haltmeier, Željko Kereta, Erich Kobler, Oscar Leong, Mohammad Sadegh Salehi, Carola-Bibiane Schönlieb, Johannes Schwab, Zakhar Shumaylov, Jeremias Sulam, German Shâma Wache, Martin Zach, Yasi Zhang, Matthias J. Ehrhardt, Sebastian Neumayer. 2025-10-02. Learning Regularization Functionals for Inverse Problems: A Comparative Study. https://arxiv.org/abs/2510.01755

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