Improved Quality of Reconstructed Images Through Sifting of Data in Statistical Image Reconstruction
Abstract
The U.S. Air Force employs adaptive optics systems to produce images of exo-atmospheric objects. Typically, a large set of short exposure images are collected, re-centered to compensate for random image motion, averaged together to improve the signal to noise ratio, and then processed to form a reconstructed image. It is known that some short exposure images will be better than others, so some researchers have suggested that image quality can be improved by selecting a subset of the short exposure images according to some quality criterion, and then processing the average of this subset to form a single, high quality image. This thesis investigates the statistical implications of using frame selection as a post-processing technique to enhance images of exo- atmospheric objects measured by Air Force adaptive optics systems. The results demonstrate that frame selection narrows the optical system point spread function, which reduces image blurring, and increases the frequency spectrum signal to noise ratio, particularly in the mid-frequency range. For extended objects, the technique is light level dependent: for a 1 meter adaptive optics telescope, frame selection will yield an increase in signal to noise ratio for objects brighter than visual magnitude +2.3. Frame Selection, Adaptive Optics, Image Processing
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1993
- Accession Number
- ADA273884
Entities
People
- Craig A. Stoudt
Organizations
- Air Force Institute of Technology