Do you feel lucky ahead of time ?

When I last mentioned the issue of super-resolution, I was under the impression that turbulence aided micro-lensing could not be used to do astronomy because the atmosphere layer was too thick. It looks as though, one can wait longer in astronomy and also obtain similar results as explained in the Lucky Image Website. But while the CCD technology is indeed impressive, much of the post processing is essential to the construction of full images. One needs to figure out automatically where the turbulence helped you and where it didn't:

There are several newsgroups that are interested in Lucky Imaging with video sources. They include:

http://groups.yahoo.com/group/videoastro/ , http://www.astronomy-chat.com/astronomy/ and http://www.qcuiag-web.co.uk/

QCUIAG is a very friendly group and visitors wanting to learn more about the techniques are pretty much guaranteed answers to their questions. Coupled with image post processing algorithms, these techniques are producing images of remarkable quality. In the UK Damian Peach is probably the most experienced in using these techniques. Some examples of his work can be seen here: http://www.damianpeach.com/

Programmes such as Astrovideo, which was originally designed to support the video stacking process developed by Steve Wainwright, the founder of QCUIAG, have frames selection algorithms, see: http://www.coaa.co.uk/astrovideo.htm

A working automated system was developed in the program K3CCDTools by QCUIAG member Peter Katreniak in 2001, see: http://qcuiag-archive.technoir.org/2001/msg03113.html

You can see the home page for K3CCDTools here: www.pk3.org/Astro/k3ccdtools.htm

And so one wonders if there would be a way to first acquire the part of the image of interest and then expect it to grow to the full image as turbulence keeps on helping you. The application would not be about astronomy where the stacking of images essentially reduce the noise to signal ratio but could be used to do imaging on earth. When observing the sky, we mostly have point-like features. If one were to decompose one of these images using wavelets, it is likely that the clearest part of the images would have the highest frequency content (besides noise). And so one of the ways to accomplish this task would be to look for parts of images with the sparsest low frequency components. Eventually when one deals with high resolution astronomy images, one is also bound to deal with curvelets and so the reasoning I just mentioned would need to be revised.


References: [1] Damien Peach's breathtaking lunar photographs.
[2] Jean-Luc Starck page.
[3] Palomar observatory lucky image press release.