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Robust and Scalable Computational Methods for Extreme Imaging. (EPS2022/56)


   School of Engineering & Physical Sciences

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  Dr A. Halimi, Dr J Leach  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Recent technological innovations, eg detection and acquisition hardware, have pushed sparse-photon imaging to the fore in a variety of applications including 3D Lidar imaging and microscopy.  3D Lidar imaging consists in sending laser pulses to a target and capturing the returned photons after reflection from the target. Recent advances in single-photon detectors allowed the use of such systems to acquire 3D images in low photon regime (few received photons) due for example to long-range km imaging or fast imaging, which constitute important challenges for automotive Lidar and sensing for autonomous vehicles. Despite recent advances, current systems can still be optimized regarding the task to be achieved such as parameters estimation, classification, etc.

This project tackles two main challenges: (i) imaging in extreme conditions due to high noise levels (e.g., imaging through obscurants) or incomplete data (e.g., sparse photon imaging in low illumination imaging, or the low resolution of data), (ii) managing the high volume of data (e.g., multimodal imaging, fusion of different sensors, 3D videos). In this context, the PhD candidate will work on the development of new strategies to improve both the acquisition and processing of single-photon data. A focus will be on the combination of statistical model-based methods with state-of-the-art deep learning algorithms to solve challenging imaging problems. The candidate will also investigate the exploitation of complementary information from different imaging sensors/modalities to improve performance (e.g., use of high-resolution RGB image to improve resolution of Lidar images). The developed methods will be validated on several sensing/imaging applications including Lidar depth imaging, microscopy imaging, hyperspectral imaging and satellite altimetry.

Through the project, the PhD candidate will learn state-of-the-art approaches regarding Bayesian modelling, deep learning architectures, non-local filtering, graph-based approaches, and optimization algorithms.  The project will be achieved in collaboration with industrial partners and system design teams in HWU which will provide additional real data.

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.

Matlab, Python, C/C++.

How to Apply

1. Important Information before you Apply

When applying through the Heriot-Watt on-line system please ensure you provide the following information:

(a) in ‘Study Option’

You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Chemistry PhD, Physics PhD, Chemical Engineering PhD, Mechanical Engineering PhD, Bio-science & Bio-Engineering PhD or Electrical PhD as appropriate and select September 2022 for study option (this can be updated at a later date if required)

(b) in ‘Research Project Information’

You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the potential supervisor’s name.

This information will greatly assist us in tracking your application.

Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts.


Funding Notes

There are a number of scholarships available which offer funding from between 3 and 3.5 years at an average stipend rate of £15,000 per year.

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, submitted.
[3] A. Ruget, S. McLaughlin, R. K. Henderson, I. Gyongy, A. Halimi, J. Leach, "Robust super-resolution depth imaging via a multi-feature fusion deep network," Optics Express, 2021.
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