From retrodiction to Bayesian quantum imaging

We employ quantum retrodiction to develop a robust Bayesian algorithm for reconstructing the intensity values of an image from sparse photocount data, while also accounting for detector noise in the form of dark counts. This method yields not only a reconstructed image but also provides the full pro...

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Bibliographic Details
Published in:Journal of optics (2010) 2017-04, Vol.19 (4), p.44001
Main Authors: Speirits, Fiona C, Sonnleitner, Matthias, Barnett, Stephen M
Format: Article
Language:English
Subjects:
Bayesian inference
image denoising
Poisson noise
quantum imaging
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Summary:We employ quantum retrodiction to develop a robust Bayesian algorithm for reconstructing the intensity values of an image from sparse photocount data, while also accounting for detector noise in the form of dark counts. This method yields not only a reconstructed image but also provides the full probability distribution function for the intensity at each pixel. We use simulated as well as real data to illustrate both the applications of the algorithm and the analysis options that are only available when the full probability distribution functions are known. These include calculating Bayesian credible regions for each pixel intensity, allowing an objective assessment of the reliability of the reconstructed image intensity values.
ISSN:2040-8978
2040-8986
DOI:10.1088/2040-8986/aa5ccf