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Robust Computational Methods for High-Dimensional Imaging


   School of Engineering & Physical Sciences

  ,  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

This project aims to develop new computational algorithms to optimize the acquisition and processing of single-photon data and enable fast imaging in extreme conditions using statistical and learning based approaches.    

Recent technological innovations, eg detection and acquisition hardware, have pushed sparse-photon imaging to the fore in a variety of applications including microscopy for bioimaging and 3D Lidar imaging for autonomous vehicles or consumer electronics. Despite recent advances, current systems can still be optimized to compress the large data volume, better exploit the noisy or incomplete measurements, and/or fusion multiple sensors to benefit from complementary information. 

In this project, we will optimize the acquisition and processing of these imaging systems to tackle these challenges. A focus will be on the combination of statistical Bayesian models [1-4], optimization algorithms and state-of-the-art deep learning methods to solve these challenging inverse problems. In particular, the candidate will investigate one or a combination of the following aspects: the design of efficient networks (eg using unrolling [2], or plug-and-play approaches), compressing data using new representations (eg using neural fields approaches or generative modelling with VAE, normalizing flows, etc), proposing robust methods for multimodal data [3]. 

The developed methods will be validated on several sensing/imaging applications including Lidar depth imaging, microscopy imaging, hyperspectral imaging and satellite altimetry. The candidate will closely collaborate with world-leading computational imaging groups in Heriot-Watt University (single-photon group, Quantum Optics and Computational Imaging), Edinburgh University and the wider community via the EPSRC Quantum Technology Hub in Quantum Imaging (Quantic). The research will be conducted in collaboration with industrials partners: Leonardo, and STMicroelectronics.

All applicants must have or expect to have a 1st class Master degree in electrical engineering, applied mathematics, physics, computer science, or a related discipline. Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or via Microsoft Teams).

More information regarding the group can be accessed in: https://sites.google.com/site/abderrahimhalimi/home

Software Needs and Skills:

Statistical signal and Image processing, Bayesian methods, deep learning, optimization.

Matlab, Python, C/C++.


References

[1] A. Halimi, A. Maccarone, R. Lamb, G. Buller, S. McLaughlin, "Robust and Guided Bayesian Reconstruction of Single-Photon 3D Lidar Data: Application to Multispectral and Underwater Imaging," IEEE-TCI, 2021.
[2] J. Koo, A. Halimi, S. McLaughlin, "A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems", IEEE-JSTSP, 2022.
[3] A. Ruget, M. Tyler, G. Mora-Martín, S. Scholes, F. Zhu, I. Gyongy, B. Hearn, S. McLaughlin, A. Halimi, J. Leach, "Pixels2Pose: Super-Resolution Time-of-Flight Imaging for 3D Pose Estimation", Science Advances, 2022
[4] M. A. A. Belmekki, R. Tobin, G. S. Buller, S. McLaughlin, A. Halimi, "Fast Task-Based Adaptive Sampling For 3D Single-Photon Multispectral LiDAR Data," IEEE-TCI, 2022.

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