Research

Single-Photon Data

Quantum Sensing · High-Sensitivity Imaging · Time-Correlated Counting

Home/Research/Single-Photon Data

Robust 3D imaging, sensor fusion, event processing, depth and intensity restoration, underwater 3D imaging.

01

Robust Lidar Imaging

Focus areas: Low illumination Lidar imaging, long range imaging, imaging through obscurants. A bi-static system with SPAD camera and laser source images targets at distances up to 150 meters through free-space optical paths with obscurant-filled environments.

Experimental setup for single-photon 3D imaging SPAD-based LiDAR system for 3D imaging through obscurants
Robust Bayesian 3D reconstruction results 3D reconstruction using Bayesian estimation methods
R. Tobin, A. Halimi, A. McCarthy, M. Laurenzis, F. Christnacher, G. S. Buller, "Three-dimensional single-photon imaging through obscurants," Optics Express, Vol. 27, Issue 4, pp. 4590-4611, 2019.

R. Tobin, A. Halimi, A. McCarthy, P. Soan, G. Buller, "Robust real-time 3D imaging of moving scenes through atmospheric obscurants using single-photon LiDAR," Scientific Reports—Nature, 2021.

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 Trans. Computational Imaging, vol. 7, pp. 961-974, Sept. 2021.
02

Underwater Lidar Imaging

The proposed Bayesian approach provides robust depth profile and reflectivity restoration even in highly scattering underwater environments.

Underwater single-photon 3D imaging Depth profile and reflectivity restoration through turbid water
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.
03

Multi-Spectral Lidar Imaging

Fast task-based adaptive sampling pipeline for 3D single-photon multispectral LiDAR data. Adaptive sampling outperforms uniform sampling by focusing acquisition on informative regions.

Adaptive sampling for multispectral LiDAR Multi-spectral LiDAR with combined depth and spectral information
A. Halimi, R. Tobin, A. McCarthy, J. Bioucas-Dias, S. McLaughlin, G.S. Buller, "Restoration of Multidimensional Sparse Single-Photon 3D-LiDAR Images," IEEE Trans. Computational Imaging, Vol. 6, July 2019.

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 Trans. Computational Imaging, vol. 8, pp. 174-187, Feb. 2022.

A. Belmekki, J. Leach, R. Tobin, G.S. Buller, S. McLaughlin, A. Halimi, "3D Target Detection and Spectral Classification for Single-photon LiDAR Data," Optics Express, 2023.
04

Multi-modal Imaging (Fusion)

High-speed 3D image sequence generation through multi-modal sensor fusion. Hybrid-mode imaging combines SPAD arrays with conventional sensors for guided upsampling.

High-speed 3D via sensor fusion Hybrid-mode imaging combining SPAD arrays with conventional sensors
Multi-modal sensor fusion results Multi-modal data integration for improved 3D reconstruction
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.

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.

A. Ruget, M. Tyler, G. Mora-Martin, 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.

S. Chen, A. Halimi, F. Zhu, I. Gyongy, R. 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," Scientific Reports, vol. 9, no. 8075, 2019.
Related Publications
  1. A. Pawlikowska, A. Halimi, R. A. Lamb, and G. S. Buller, "Single-photon three-dimensional imaging at up to 10 kilometers range," Optics Express, vol. 25, no. 10, May 2017.
  2. R. Tobin, A. Halimi, A. McCarthy, X. Ren, K. McEwan, S. McLaughlin, G. S. Buller, "Long-range depth profiling of camouflaged targets using single-photon detection," Optical Engineering, vol. 57, no. 3, Dec. 2017.
  3. X. Ren, P. Connolly, A. Halimi, Y. Altmann, S. McLaughlin, I. Gyongy, R. K. Henderson, G. S. Buller, "High-resolution depth profiling using a range-gated Si CMOS SPAD quanta image sensor," Optics Express, vol. 26, no. 5, 2018.
  4. S. Chen, A. Halimi, X. Ren, A. McCarthy, X. Su, S. McLaughlin, G.S. Buller, "Learning Non-Local Spatial Correlations To Restore Sparse 3D Single-Photon Data," IEEE Trans. Image Processing, Vol. 29, Dec. 2019.
  5. A. Wallace, A. Halimi, G.S. Buller, "Full Waveform LiDAR for Adverse Weather Conditions," IEEE Trans. Vehicular Technology, Vol. 69, Issue 7, July 2020.
  6. K. Kuzmenko et al., "3D LIDAR imaging using Ge-on-Si single photon avalanche diode detectors," Optics Express, Vol. 22, Issue 2, Jan. 2020.
  7. J. Koo, A. Halimi, S. McLaughlin, "A Bayesian Based Deep Unrolling Algorithm for Single-Photon Lidar Systems," IEEE-JSTSP, 2022.
  8. S. Plosz, A. Maccarone, S. McLaughlin, G. S. Buller, A. Halimi, "Real-Time Reconstruction of 3D Videos from Single-Photon LiDaR Data in the Presence of Obscurants," IEEE Trans. Computational Imaging, vol. 9, pp. 106-119, Feb. 2023.
  9. G. Mora-Martin, A. Turpin, A. Ruget, A. Halimi, R. Henderson, J. Leach, I. Gyongy, "High-speed object detection with a single-photon time-of-flight image sensor," Optics Express, 2021.
  10. R. Tobin, A. Halimi, A. McCarthy, P. Soan, G. Buller, "Robust real-time 3D imaging of moving scenes through atmospheric obscurants using single-photon LiDAR," Scientific Reports—Nature, 2021.