New Unified Framework for Superresolution Imaging with Prior Information
Abstract
The central objective of this project is to develop a new rigorous and unified mathematical framework for super-resolution imaging that can outperform existing techniques by judicious exploitation of prior information on the desired image. Super-resolution refers to the ability to extract finer details (typically high frequency components) of a desired signal from low-resolution easurements. This is a central imaging problem with applications in single molecule microscopy, radar and sonar target localization, and neural signal processing. This problem is highly ill-posed and the underlying signal cannot be recovered unless suitable priors on the desired signal are available. This proposal will address a large class of super-resolution problems across Different modalities (such as microscopy and radar source localization), by showing the unifying role played by correlation priors in pushing the existing resolution limits of super-resolution imaging. New mathematical insights into such correlation-aware algorithms will be developed to further enhance their performance. By identifying the common structural constraints that these algorithms need to satisfy, the proposal also puts forward a unified analysis framework to analyze the performance of these algorithms. Explicit error metrics will be developed which will serve as universal upper bounds on the performance of any super-resolution algorithm utilizing correlation priors. These results will shed new light into super-resolution imaging techniques with enhanced resolution limits, and can potentially become an integral part of the sensing and imaging technology for the operation of U.S. Navy mission-critical systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 25, 2019
- Source ID
- N000141912256
Entities
People
- Piya Pal
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego