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.

翻译：近年来，针对成像逆问题求解的多种学习型正则化框架相继涌现。这些框架在提供灵活建模能力的同时，也蕴含着深刻的数学洞见。现有方法在架构设计与训练策略上存在差异，且因其非模块化的实现方式而难以直接比较。为弥补这一空白，我们将现有代码收集并整合至统一框架中。这种统一视角使我们能够系统比较各类方法，揭示其优势与局限，从而为其未来潜力提供有价值的见解。此外，我们还为每种方法提供了简明描述，并辅以实用指导原则。