Collaborative Research: Imaging in the Presence of Dynamic Distortions: Fundamental Limits and Practical Approaches
Abstract
Over the last two decades, there has been tremendous progress in characterizing and understanding the information theoretical limits of imaging. However, the majority of this analysis has relied on static and weak distortions. There is very limited characterization and understanding regarding the fundamental limits of sensing and imaging in the presence of strong and dynamic distortions, aberrations and interference. This project aims to develop new tools and theory to analyze and quantify the fundamental limits of imaging through dynamic distortions with completely unknown and partially known distortion characteristics. Specifically, we are interested in characterizing the fundamental, algorithm-independent limits of (1) sensing static targets through dynamic distortions, (2) sensing dynamic targets through dynamic distortions, and (3) sensing static/dynamic targets in the presence of time-varying cooperativeand adversarial active-illumination/interference. In addition, we will develop practical algorithms that attempt to achieve these fundamental limits of performance and test novel computational imaging systems capable of probing the practical limits of imaging through dynamic distortions. A key step in our approach will be the introduction of new metrics that provide a unified framework for describing and analyzing the dynamics of any measurement/distortion process. These generalized descriptors will allow us to analyze and improve the rich variety of imaging systems which suffer from dynamic distortions and (partially) unknown forward models.We anticipate the tools we develop will lead to practical algorithms for imaging through dynamic distortions and interference---practical algorithms that can be adapted to a variety of important application domains such as imaging through turbulence, imaging through scattering media, underwater imaging and synthetic aperture radar imaging.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 24, 2023
- Source ID
- N000142312752
Entities
People
- Christopher A. Metzler
Organizations
- Office of Naval Research
- United States Navy
- University of Maryland