Bayesian Restoration of high-dimensional photon-starved images

Julián Tachella, Yoann Altmann, Marcelo Pereyra, Stephen McLaughlin, Jean-Yves Tourneret

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Abstract

This paper investigates different algorithms to perform image restoration from single-photon measurements corrupted with Poisson noise. The restoration problem is formulated in a Bayesian framework and several state-of-the-art Monte Carlo samplers are considered to estimate the unknown image and quantify its uncertainty. The different samplers are compared through a series of experiments conducted with synthetic images. The results demonstrate the scaling properties of the proposed samplers as the dimensionality of the problem increases and the number of photons decreases. Moreover, our experiments show that for a certain photon budget (i.e., acquisition time of the imaging device), downsampling the observations can yield better reconstruction results.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages747-751
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Electronic)2076-1465

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  • Cite this

    Tachella, J., Altmann, Y., Pereyra, M., McLaughlin, S., & Tourneret, J-Y. (2018). Bayesian Restoration of high-dimensional photon-starved images. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 747-751). (European Signal Processing Conference (EUSIPCO)). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553175