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STARBLADE: STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission

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Ada Coda
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STARBLADE: STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission

Postby Ada Coda » Wed May 09, 2018 3:33 pm

STARBLADE: STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission

Abstract: STARBLADE (STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission) separates superimposed point-like sources from a diffuse background by imposing physically motivated models as prior knowledge. The algorithm can also be used on noisy and convolved data, though performing a proper reconstruction including a deconvolution prior to the application of the algorithm is advised; the algorithm could also be used within a denoising imaging method. STARBLADE learns the correlation structure of the diffuse emission and takes it into account to determine the occurrence and strength of a superimposed point source.

Credit: Knollmüller, Jakob; Frank, Philipp; Ensslin, Torsten A.

Site: https://gitlab.mpcdf.mpg.de/ift/starblade
http://adsabs.harvard.edu/abs/2018arXiv180405591K

Bibcode: 2018ascl.soft05009K

ID: ascl:1805.009

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