Conventional wisdom holds that pulsed lidar cannot directly measure velocity. We present for the first time a method for measuring range and velocity simultaneously using pulsed single-photon lidar. A moving target’s radial velocity manifests as a Doppler shift in the repetition frequency of the received laser pulse train. Doppler SPL has the potential to introduce velocity sensing capabilities to applications such as atmospheric monitoring or autonomous navigation.

Work with R. Kitichotkul, Y. Ma, H. Mansour

• Optica paper: Simultaneous range and velocity measurement with Doppler single-photon lidar

• ICASSP 2025 paper: Doppler Single-Photon Lidar

Optical coherence tomography is a well-established volumetric imaging modality for translucent media. In this work, we explore using OCT for estimating the 3D profile of surfaces. In particular, we develop methods based on the Maximum Likelihood Estimator for a surface depth, which yields high-quality reconstructions of micrometer-scale objects. This technology is of great interest for component inspection in factory automation processes.

Work with H. Mansour, P. Boufounos, P. Orlik, T. Koike-Akino, K. Parsons

• IEEE TCI paper: Multi-layered surface estimation for low-cost optical coherence tomography

• ICIP 2022 paper: Maximum likelihood surface profilometry via optical coherence tomography

Single-pixel imaging (SPI) techniques are advantageous for gas sensing because mid-infrared detectors are expensive. Here, we propose to image spatio-temporal flows from incomplete SPI measurements by leveraging scene priors in the form of a reduced-order model (ROM) of the dynamics learned from training data examples. The ROM prior, implemented as a neural ordinary differential equation, allows us to achieve high-quality image sequence reconstruction from a small number of SPI measurements.

Work with A. Sholokhov, S. Nabi, S. Brunton, J. N. Kutz, H. Mansour, P. Boufounos

• IEEE TCI paper: Single-pixel imaging of spatio-temporal flows using differentiable latent dynamics

• ICASSP 2024 paper: Single-pixel imaging of dynamic flows using neural ODE regularization

Compton camera imaging typically ignores the ambiguity in the Compton cone axis introduced by the detector pixel size. We introduce approximations to the axis ambiguity that make the description of the source ambiguity computationally tractable and improve the accuracy of the detection forward model.

Work with Y. Ma, H. Mansour, P. Boufounos

• ANIMMA 2023 paper: A model of spatial resolution uncertainty for Compton camera imaging

The critical challenge of non-line-of-sight (NLOS) imaging is that diffuse reflections scatter light in all directions. The most effective existing methods use time-resolved acquisition to measure the transient response of a scene to a pulsed light source, but require large scanning large virtual apertures with many measurement points in order to perform reconstructions. 

We propose a method for seeing around corners that uses visible vertical edges to recover directional information, combined with transient measurements. Our method enables the imaging of large-scale scenes with a large field-of-view, despite a small scanning aperture and few measurement locations.

Work with Charles Saunders, Julián Tachella, John Murray-Bruce, Yoann Altmann, Jean-Yves Tourneret, Stephen McLaughlin, Robin Dawson, Franco Wong & Vivek Goyal

• Nature Communications paper: Seeing Around Corners with Edge-Resolved Transient Imaging

• ICIP 2021 paper: Edge-Resolved Transient Imaging: Performance Analyses, Optimizations, and Simulations

Single-photon detectors used in time-correlated single photon counting (e.g., for lidar, fluorescence lifetime imaging) have a dead time after each photon detection, which blocks registration of subsequent photons arriving within that dead time and causes a distortion of the detection time distribution. Our work enables fast data acquisition with dead time-limited detectors by deriving estimators that depend on accurately modeling detection time sequences as Markov chains.

Work with Yanting Ma, Robin Dawson & Vivek Goyal

• Optica paper: High-flux single-photon lidar

• IEEE Trans. Signal Processing paper: Dead Time Compensation for High-Flux Ranging

• CLEO 2020 paper: Predicting Dead Time Distortion for High-Flux Single-Photon Lidar

• ICASSP 2019 paper: Dead Time Compensation for High-Flux Depth Imaging

• OSA Math 2019 paper: Markov Chain Modeling for High-Flux Single-Photon Detection with Dead Times

In single photon detection systems (especially SPAD arrays) that have time bins longer than typical laser pulse durations, the resulting measurement errors are dominated by quantization. Our work proposes an optical time-of-flight system that uses subtractive dither to improve image depth resolution and investigates how modeling the measurement noise with a generalized Gaussian distribution can further improve estimation error.

Work with Robin Dawson & Vivek Goyal

• Optics Express paper: Dithered Depth Imaging

• IEEE Trans. Signal Processing paper: Estimation From Quantized Gaussian Measurements: When and How to Use Dither

• ICIP 2018 paper: Improving Lidar Depth Resolution With Dither (Winner of a Best Student Paper award)

• COSI 2018 paper: Dither-Enhanced Lidar

Recent photon-efficient computational imaging methods are remarkably effective with as little as 1 detected photon per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that emphasizes the unmixing of contributions from signal and noise sources and has defined the state-of-the-art performance for SBR as low as 1/25.

Work with Vivek Goyal

• IEEE Trans. Computational Imaging paper: A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging (Winner of a 2020 IEEE SPS Young Author Best Paper Award)