Image reconstruction from photon sparse data

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
46 Downloads (Pure)


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
Publication statusPublished - 7 Feb 2017

ASJC Scopus subject areas

  • General


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