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 language | English |
|---|---|
| Title of host publication | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
| Publisher | IEEE |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538612514 |
| ISBN (Print) | 9781538612521 |
| DOIs | |
| Publication status | Published - 12 Mar 2018 |
| Event | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Curacao, Netherlands Duration: 10 Dec 2017 → 13 Dec 2017 http://www.cs.huji.ac.il/conferences/CAMSAP17/ |
Conference
| Conference | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing |
|---|---|
| Abbreviated title | CAMSAP 2017 |
| Country | Netherlands |
| City | Curacao |
| Period | 10/12/17 → 13/12/17 |
| Internet address |
Fingerprint
Keywords
- single-photon counting
- Image processing
- Bayesian inference
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - Unsupervised restoration of subsampled images constructed from geometric and binomial data
AU - Altmann, Yoann
AU - McLaughlin, Stephen
AU - Padgett, Miles J.
PY - 2018/3/12
Y1 - 2018/3/12
N2 - 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.
AB - 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.
KW - single-photon counting
KW - Image processing
KW - Bayesian inference
UR - http://www.scopus.com/inward/record.url?scp=85050734368&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP.2017.8313187
DO - 10.1109/CAMSAP.2017.8313187
M3 - Conference contribution
SN - 9781538612521
SP - 1
EP - 5
BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
PB - IEEE
ER -