Unsupervised restoration of subsampled images constructed from geometric and binomial data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate a new imaging denoising algorithm for single-photon applications where the classical Poisson noise assumption does not hold. Precisely, we consider two different acquisition scenarios where the unknown intensity profile is to be recovered from subsampled measurements following binomial or geometric distributions, whose parameters are nonlinearly related to the intensities of interest. Adopting a Bayesian approach, a flexible prior model is assigned to the unknown intensity field and an adaptive Markov chain Monte Carlo methods is used to perform Bayesian inference. In particular, it allows us to automatically adjust the amount of regularisation required for satisfactory image inpainting/restoration. The performance of the proposed model/method is assessed quantitatively through a series of experiments conducted with controlled data and the results obtained are very promising for future analysis of multidimensional single-photon images.

Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
ISBN (Print)9781538612521
DOIs
Publication statusPublished - 12 Mar 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Curacao, Netherlands
Duration: 10 Dec 201713 Dec 2017
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP 2017
CountryNetherlands
CityCuracao
Period10/12/1713/12/17
Internet address

Fingerprint

Restoration
Photons
Image reconstruction
Markov processes
Monte Carlo methods
Imaging techniques
Experiments

Keywords

  • single-photon counting
  • Image processing
  • Bayesian inference

Cite this

Altmann, Y., McLaughlin, S., & Padgett, M. J. (2018). Unsupervised restoration of subsampled images constructed from geometric and binomial data. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (pp. 1-5). IEEE. https://doi.org/10.1109/CAMSAP.2017.8313187
Altmann, Yoann ; McLaughlin, Stephen ; Padgett, Miles J. / Unsupervised restoration of subsampled images constructed from geometric and binomial data. 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2018. pp. 1-5
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Altmann, Y, McLaughlin, S & Padgett, MJ 2018, Unsupervised restoration of subsampled images constructed from geometric and binomial data. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, pp. 1-5, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Curacao, Netherlands, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313187

Unsupervised restoration of subsampled images constructed from geometric and binomial data. / Altmann, Yoann; McLaughlin, Stephen; Padgett, Miles J.

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2018. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Altmann Y, McLaughlin S, Padgett MJ. Unsupervised restoration of subsampled images constructed from geometric and binomial data. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE. 2018. p. 1-5 https://doi.org/10.1109/CAMSAP.2017.8313187