Surface Profile Estimation via Optical Coherence Tomography
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 opaque 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
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
Dead-time effects in Single-photon Lidar
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
Dithered depth measurement
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
Robust single-photon 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)