Data for: "Image retrodiction at low light levels"

  • Matthias Sonnleitner (Creator)
  • John Jeffers (Creator)
  • Stephen M. Barnett (University of Glasgow) (Creator)



Imaging technologies working at very low light levels acquire data by counting the number of photons impinging on each pixel. Especially in cases with, on average, less than one photocount per pixel, the resulting images are heavily corrupted by Poissonian noise. To tackle this problem, we use methods from Bayesian statistics to retrodict the spatial intensity distribution responsible for the photocount measurements. Unlike the usual photon-limited image denoising algorithms, we calculate the full probability distributions for the intensities at each pixel. The knowledge of these probability distributions helps to assess the validity of results from image analysis using data corrupted by Poisson noise with low photon-count numbers and dark counts.

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Date made available20 Dec 2022

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