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Computational Imaging: Improving the Acquisition and Processing of Single-Photon Data

Project Description

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.

In this project, we will consider optimizing both the acquisition and processing of the single-photon data to achieve better performance in estimating the parameters of interest (e.g., depth and reflectivity information from a target in 3D imaging). The PhD candidate will improve the acquisition by studying an adaptive sampling strategy to select and acquire informative data regarding the estimation task, which will lead to a non-uniform sampling of the data. Due to the challenging acquisition conditions used in this project, we will need to restore the acquired images by considering advanced statistical methods. Thanks to their good denoising performance, non-local restoration approaches appear as a promising candidate to achieve the denoising thanks to their established good performance (see examples in The latter will be considered in this project by building on recent work in our group using a graph approach to define a neighbourhood structure, and a Bayesian approach to achieve a robust restoration. The developed methods will be generalized to multidimensional data to account for multi-spectral data, multi-frame data (3D videos) and will be applied to both 3D imaging and microscopy. Ultimately, the project will lead to a new generation of smart imaging systems using an optimized acquisition and processing of the data.

Through the project, the PhD student will learn state-of-the-art approaches regarding data sampling, Bayesian modelling, 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.

Interested students are strongly invited to see: for more details about the project and related ongoing research.

Funding Notes

The PhD fund is available for 3 years preferably for UK and EU Students. Fund includes stipends of ~£14.5k per year and full tuition fees covered for 3 years. To apply, please send your CV, a cover letter explaining your motivation for applying to this position, academic transcripts, and contact details of at least two referees to Dr Abderrahim Halimi ().


A. Halimi, A. Maccarone, A. McCarthy, S. McLaughlin and G. S. Buller, "Object Depth Profile and Reflectivity Restoration from Sparse Single-Photon Data Acquired in Underwater Environments," IEEE Trans. Computational Imaging, vol. 3, no. 3, Sept. 2017.

S.Chan, A. Halimi, F. Zhu, I. Gyongy, R. K. Henderson, R. Bowman, S. McLaughlin, G. S. Buller and J. Leach, "Long-range depth imaging using a single-photon detector array and non-local data fusion," submitted to Nature Scientific Report, 2018.

A. Halimi, X. Ren, A. McCarthy, J. Bioucas-Dias, S. McLaughlin, and G. S. Buller, "Restoration of Multilayered Single-Photon 3D LiDAR Images," in IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Sheffield, UK, July 2018.

A. Halimi, P. Connolly, X. Ren, Y. Altmann, I. Gyongy, R. K. Henderson, S. McLaughlin and G. S Buller "Restoration of Depth and Intensity Images using a Graph Laplacian Regularization," in IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curaçao, Dutch Antilles, Dec. 2017.

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