This paper attempts to provide an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one-dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data consistent solutions through explicit guidance to satisfy specific semantic or textural properties.

翻译：本文试图综述当前利用变分方法和机器学习解决成像逆问题的各种方法。特别关注点估计量及其对抗扰动的鲁棒性。在此背景下，本文提供了一维模拟问题的数值实验结果，展示了不同方法的鲁棒性，并实证验证了理论保证。本综述的另一重点是，通过显式引导以满足特定语义或纹理属性，探索数据一致解的子空间。