Adaptive Sensing for High-throughput Imaging
Abstract
This project aims to assess the capabilities of capturing and processing very large-scale image datasets coupled to powerful computational backends. We will develop exible tools for design-ing maximally informative capture strategies for multi-dimensional object reconstruction. We will design and build high-throughput multi-sensor image acquisition systems coupled to optimization- based inverse algorithms. We will derive theory for algorithmic self-calibration, in which the hard- ware system is calibrated from image data, making reconstruction more robust and practical. Finally, we will explore ideas of adaptive system design for sparse measurements, as they pertain to large-scale acquisition. The resulting systems and theory that will be developed represent a new era of increased image data throughput, from gigascale/second towards terascale/second. The ideas and devices generated should have use in various naval applications including object tracking, surveillance and imaging in degraded environments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 05, 2017
- Source ID
- N000141712401
Entities
People
- Laura Waller
Organizations
- Office of Naval Research
- United States Navy
- University of California Regents