Generative Models for Inverse Problems
Abstract
Inverse problems are ubiquitous in scientific and engineering applications, where the goal is to infer the properties of a system from indirect measurements. In recent years, generative models have shown remarkable success in a variety of tasks, such as image synthesis and data compression. However, their application to inverse problems remains largely unexplored. In this project, we propose a novel framework for solving inverse problems using generative models, which offers significant advantages over traditional approaches. We propose to investigate several theoretical and numerical aspects related to the use of generative models techniques in inverse problems with the aim of improving the reconstruction methods to bring many emerging imaging modalities closer to actual usage.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 22, 2024
- Source ID
- FA86552317083
Entities
People
- Matteo Santacesaria
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Genoa