Computational imaging: Robust and Scalable Algorithms for 3D Lidar videos (2020/21)

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, our image processing group will work closely with system design groups in Heriot-Watt to combine multi-sensor data acquired at high frame rates using a 3D Lidar system and another sensing modality such as a passive optical imaging system or a radar. This combination aims to reduce the noise affecting the 3D images (due to imaging through fog, rain) and to improve the spatial resolution of 3D Lidar videos. Current solutions developed by our group show promising results at high frame rates (acquisition at 500 frames per second in [1]) and the student will generalize them to account for high levels of noise accounted in real world applications (see [2,3]) and to reach real time performance using parallel computing tools.

Through the project, the student will learn state-of-the-art approaches regarding Bayesian modelling, machine learning, 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.

Keywords: Statistical signal and Image processing, Bayesian methods, Matlab, or C++, Cuda

[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] I. Gyongy, S. W. Hutchings, A. Halimi, M. Tyler, S. Chan, F. Zhu, S. McLaughlin, R. K. Henderson, and J. Leach, "High-speed 3D sensing via hybrid-mode imaging and guided upsampling," Optica, Vol. 7, Issue 10, Sept. 2020.
[3] A. Wallace, A. Halimi, G.S. Buller, "Full Waveform LiDAR for Adverse Weather Conditions," IEEE Trans. vehicular Technology, Vol. 69, Issue 7, July 2020.
[4] . 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.

Supervisor name: 
Dr Abderrahim Halimi