Improving Closely Spaced Dim Stellar Objects Through Improved Multi-Frame Blind Deconvolution
Abstract
This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed to overcome the photon counting noise and the random and turbulent atmospheric conditions. The performance of the algorithms are compared with that of the Multi-Frame Blind Deconvolution. The new algorithms are tested and validated with computer generated data. The Neighborhood System Blind Deconvolution is also modified to overcome the undersampling effects since it is validated on the undersampled laboratory collected data. Even though the algorithms are designed for ground to space imaging systems, the same concept can be extended for space to space imaging. This research provides two better techniques to improve closely space dim object detection.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2020
- Accession Number
- AD1113917
Entities
People
- Ronald M. Aung
Organizations
- Air Force Institute of Technology