Frame selection performance limits for statistical image reconstruction of adaptive optics compensated images

Abstract

Adaptive optics systems have been used to compensate for the degrading effects of atmospheric turbulence in images collected with large astronomical telescopes. Postdetection processing techniques are also employed to further improve adaptive optics compensated images. Typically, many short-exposure images are collected, recentered to compensate for tilt, and then averaged to overcome randomness in the images and improve signal-to-noise ratio (SNR). Experience shows that some short-exposure images in a data set are better than others. The frame selection postdetection processing technique uses an image quality metric to discard low-quality frames and improve image spectrum SNR. We address key issues pertaining to frame selection performance limits. Noise trade-offs are used to investigate minimum object brightness for successful application of the frame selection technique. Limits imposed by noise effects result in a minimum object brightness of apparent visual magnitude +8 for point sources and +4 for a +2-m length representative extended object imaged with a 1-m-diam telescope with no central obscuration. Effective average point spread functions for the point source and the representative extended object after frame selection processing under equivalent seeing conditions are almost identical. Thus, deconvolution could be applied to these images obtained via frame selection.