TL;DR
The Thresher introduces an online multi-frame blind deconvolution method for lucky imaging that utilizes all data to improve signal-to-noise ratio without sacrificing angular resolution, outperforming traditional lucky imaging techniques.
Contribution
It presents a novel image analysis pipeline that leverages all available data in lucky imaging, avoiding prior PSF estimates and optimizing the scene estimate based on a likelihood function.
Findings
Outperforms traditional lucky imaging in signal-to-noise ratio.
Maintains angular resolution by accounting for individual-frame PSFs.
Effective on both simulated and real telescope data.
Abstract
In traditional lucky imaging (TLI), many consecutive images of the same scene are taken with a high frame-rate camera, and all but the sharpest images are discarded before constructing the final shift-and-add image. Here we present an alternative image analysis pipeline -- The Thresher -- for these kinds of data, based on online multi-frame blind deconvolution. It makes use of all available data to obtain a best estimate of the astronomical scene in the context of reasonable computational limits; it does not require prior estimates of the point-spread functions in the images, or knowledge of point sources in the scene that could provide such estimates. Most importantly, the scene it aims to return is the optimum of a justified scalar objective based on the likelihood function. Because it uses the full set of images in the stack, The Thresher outperforms TLI in signal-to-noise; as it…
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