IMAGE-SPECTRUM SIGNAL-TO-NOISE-RATIO IMPROVEMENTS BY STATISTICAL FRAME SELECTION FOR ADAPTIVE-OPTICS IMAGING THROUGH ATMOSPHERIC-TURBULENCE

Adaptive-optics systems have been used to overcome some of the effects of atmospheric turbulence on large-aperture astronomical telescopes. However, the correction provided by adaptive optics cannot restore dif fraction-limited performance, due to discretized spatial sampling of t he wavefront, limited degrees of freedom in the adaptive-optics system , and wavefront sensor measurement noise. Field experience with adapti ve-optics imaging systems making short-exposure image measurements has shown that some of the images are better than others in the sense tha t the better images have higher resolution. This is a natural conseque nce of the statistical nature of the compensated optical transfer func tion in an adaptive-optics telescope. Hybrid imaging techniques have b een proposed that combine adaptive optics and postdetection image proc essing to improve the high-spatial-frequency information of images. Pe rformance analyses of hybrid methods have been based on prior knowledg e of the ensemble statistics of the underlying random process. Improve d image-spectrum SNRs have been predicted, and in some cases experimen tally demonstrated. In this paper we address the issue of selecting an d processing the best images from a finite data set of compensated sho rt-exposure images. Image sharpness measures are used to select the da ta subset to be processed. Comparison of the image-spectrum SNRs for t he cases of processing the entire data set and processing only the sel ected subset of the data shows a broad range of practical cases where processing the selected subset results in superior SNR.