Image reconstruction from photon sparse data

Lena Mertens, Matthias Sonnleitner, Jonathan Leach, Megan Agnew, Miles J. Padgett

Research output: Contribution to journalArticle

10 Citations (Scopus)
32 Downloads (Pure)

Abstract

We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.

Original languageEnglish
Article number42164
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 7 Feb 2017

ASJC Scopus subject areas

  • General

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    Mertens, L., Sonnleitner, M., Leach, J., Agnew, M., & Padgett, M. J. (2017). Image reconstruction from photon sparse data. Scientific Reports, 7, [42164]. https://doi.org/10.1038/srep42164