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pro vyhledávání: '"Mclaughlin, Stephen"'
Deploying 3D single-photon Lidar imaging in real world applications faces several challenges due to imaging in high noise environments and with sensors having limited resolution. This paper presents a deep learning algorithm based on unrolling a Baye
Externí odkaz:
http://arxiv.org/abs/2307.12700
Autor:
Belmekki, Mohamed Amir Alaa, Leach, Jonathan, Tobin, Rachael, Buller, Gerald S., Mclaughlin, Stephen, Halimi, Abderrahim
3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and a very high volume of data. This is particularly evident wh
Externí odkaz:
http://arxiv.org/abs/2302.09730
Autor:
Abdulaziz, Abdullah, Mekhail, Simon Peter, Altmann, Yoann, Padgett, Miles J., McLaughlin, Stephen
Conventional endoscopes comprise a bundle of optical fibers, associating one fiber for each pixel in the image. In principle, this can be reduced to a single multimode optical fiber (MMF), the width of a human hair, with one fiber spatial-mode per im
Externí odkaz:
http://arxiv.org/abs/2210.13883
Deploying 3D single-photon Lidar imaging in real world applications faces multiple challenges including imaging in high noise environments. Several algorithms have been proposed to address these issues based on statistical or learning-based framework
Externí odkaz:
http://arxiv.org/abs/2201.10910
Autor:
Abdulaziz, Abdullah, Zhou, Jianxin, Fang, Ming, McLaughlin, Stephen, Di Fulvio, Angela, Altmann, Yoann
Publikováno v:
In Annals of Nuclear Energy 1 September 2024 204
This paper presents a scalable approximate Bayesian method for image restoration using total variation (TV) priors. In contrast to most optimization methods based on maximum a posteriori estimation, we use the expectation propagation (EP) framework t
Externí odkaz:
http://arxiv.org/abs/2110.01585
Autor:
Belmekki, Mohamed Amir Alaa, Tobin, Rachael, Buller, Gerald S., McLaughlin, Stephen, Halimi, Abderrahim
3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge to LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that
Externí odkaz:
http://arxiv.org/abs/2109.01743
Automatic network management driven by Artificial Intelligent technologies has been heatedly discussed over decades. However, current reports mainly focus on theoretic proposals and architecture designs, works on practical implementations on real-lif
Externí odkaz:
http://arxiv.org/abs/2106.13367
This paper presents a new Expectation Propagation (EP) framework for image restoration using patch-based prior distributions. While Monte Carlo techniques are classically used to sample from intractable posterior distributions, they can suffer from s
Externí odkaz:
http://arxiv.org/abs/2106.15327