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 the 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 ratio; as it accounts for the individual-frame PSFs, it does this without loss of angular resolution. We demonstrate the effectiveness of our algorithm on both simulated data and real Electron-Multiplying CCD images obtained at the Danish 1.54-m telescope (hosted by ESO, La Silla). We also explore the current limitations of the algorithm, and find that for the choice of image model presented here, non-linearities in flux are introduced into the returned scene. Ongoing development of the software can be viewed at https://github.com/jah1994/TheThresher.
Original language | English |
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Pages (from-to) | 5372-5384 |
Number of pages | 13 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 511 |
Issue number | 4 |
Early online date | 17 Feb 2022 |
DOIs | |
Publication status | Published - Apr 2022 |
Keywords
- Instrumentation: detectors
- Methods: data analysis
- Techniques: image processing
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The Thresher: lucky imaging without the waste (code)
Hitchcock, J. A. (Creator), GitHub, 2022
https://github.com/jah1994/TheThresher
Dataset: Software