Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview

• URL: http://arxiv.org/abs/2402.12072v2
• Date: Tue, 9 Jul 2024 07:13:56 GMT
• Title: Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
• Authors: Alexander Auras, Kanchana Vaishnavi Gandikota, Hannah Droege, Michael Moeller,
• Abstract summary: This paper provides 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.
• Score: 47.34359815600974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper provides 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.

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