统一对比图像逆问题中的学习正则化方法，厘清各自优劣。
Learning Regularization Functionals for Inverse Problems: A Comparative Study
- 构建统一代码框架，实现不同正则化方法的可比性。
- 系统比较多种方法在建模灵活性与稳定性上的差异。
- 为研究者提供清晰的方法指南与选型参考。
近年来，针对成像中逆问题的学习正则化框架不断涌现，兼具建模灵活性与数学可解释性。然而，各方法在架构设计和训练策略上差异显著，且实现非模块化，导致直接比较困难。本文通过收集并整合现有代码，构建统一框架，使各类方法得以系统性对比，揭示其优势与局限，为未来发展方向提供关键洞察。同时，对每种方法提供简洁说明，并附实用建议。
原文摘要 · Abstract (English)
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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