Description
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.
This site includes records provided by Elsevier's Data Monitor product. University of Strathclyde does not control or guarantee the accuracy, relevance, or completeness of the information contained in such records and accepts no responsibility or liability for such information.
This site includes records provided by Elsevier's Data Monitor product. University of Strathclyde does not control or guarantee the accuracy, relevance, or completeness of the information contained in such records and accepts no responsibility or liability for such information.
Date made available | 20 Dec 2022 |
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Publisher | figshare |