27 research outputs found
Quantum-inspired computational imaging
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip
3D Target Detection and Spectral Classification for Single-photon LiDAR Data
3D single-photon LiDAR imaging has an important role in many applications.
However, full deployment of this modality will require the analysis of low
signal to noise ratio target returns and a very high volume of data. This is
particularly evident when imaging through obscurants or in high ambient
background light conditions. This paper proposes a multiscale approach for 3D
surface detection from the photon timing histogram to permit a significant
reduction in data volume. The resulting surfaces are background-free and can be
used to infer depth and reflectivity information about the target. We
demonstrate this by proposing a hierarchical Bayesian model for 3D
reconstruction and spectral classification of multispectral single-photon LiDAR
data. The reconstruction method promotes spatial correlation between
point-cloud estimates and uses a coordinate gradient descent algorithm for
parameter estimation. Results on simulated and real data show the benefits of
the proposed target detection and reconstruction approaches when compared to
state-of-the-art processing algorithm
Purdue Conference on Active Nonproliferation
One major problem with nuclear security measurements involves source identification inthe presence of low signal-to-background ratio. This scenario iscommon to several applications, ranging from radiation identification atportal monitors to radiation source search with unmanned vehicles. In this context of identification of a large variety of sources, including natural and medical sources, sensitive sources of particular interest, but also potentially new/unknown sources for which no reference measurement is available, statistical methods are particularly appealing for their ability to capture the random nature of the measurements. Among them, Bayesian methods form a generic framework allowing for uncertainty quantification and propagation, which is of prime interest for detection (of known and unknown sources), classification, and quantification of smuggled nuclear and radiological materials. We demonstratethe use of Bayesian models for the identificationof mixed gamma sources, measured with organic scintillatorswithinshort acquisition times. We alsocompare the estimation performance using two different materials: liquid EJ-309 and stilbene crystal
Bayesian methods for inverse problems with point clouds : applications to single-photon lidar
Single-photon light detection and ranging (lidar) has emerged as a prime candidate technology for
depth imaging through challenging environments. This modality relies on constructing, for each
pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. The
problem of estimating the number of imaged surfaces, their reflectivity and position becomes very
challenging in the low-photon regime (which equates to short acquisition times) or relatively high
background levels (i.e., strong ambient illumination).
In a general setting, a variable number of surfaces can be observed per imaged pixel. The
majority of existing methods assume exactly one surface per pixel, simplifying the reconstruction problem so that standard image processing techniques can be easily applied. However, this
assumption hinders practical three-dimensional (3D) imaging applications, being restricted to controlled indoor scenarios. Moreover, other existing methods that relax this assumption achieve
worse reconstructions, suffering from long execution times and large memory requirements.
This thesis presents novel approaches to 3D reconstruction from single-photon lidar data, which
are capable of identifying multiple surfaces in each pixel. The resulting algorithms obtain new
state-of-the-art reconstructions without strong assumptions about the sensed scene. The models
proposed here differ from standard image processing tools, being designed to capture correlations
of manifold-like structures.
Until now, a major limitation has been the significant amount of time required for the analysis
of the recorded data. By combining statistical models with highly scalable computational tools
from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor
scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad
daylight from distances up to 320 m. This has enabled robust, real-time target reconstruction
of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D
imaging applications
Robust Bayesian target detection algorithm for depth imaging from sparse single-photon data
This paper presents a new Bayesian model and associated algorithm for depth
and intensity profiling using full waveforms from time-correlated single-photon
counting (TCSPC) measurements in the limit of very low photon counts (i.e.,
typically less than 20 photons per pixel). The model represents each Lidar
waveform as an unknown constant background level, which is combined in the
presence of a target, to a known impulse response weighted by the target
intensity and finally corrupted by Poisson noise. The joint target detection
and depth imaging problem is expressed as a pixel-wise model selection and
estimation problem which is solved using Bayesian inference. Prior knowledge
about the problem is embedded in a hierarchical model that describes the
dependence structure between the model parameters while accounting for their
constraints. In particular, Markov random fields (MRFs) are used to model the
joint distribution of the background levels and of the target presence labels,
which are both expected to exhibit significant spatial correlations. An
adaptive Markov chain Monte Carlo algorithm including reversible-jump updates
is then proposed to compute the Bayesian estimates of interest. This algorithm
is equipped with a stochastic optimization adaptation mechanism that
automatically adjusts the parameters of the MRFs by maximum marginal likelihood
estimation. Finally, the benefits of the proposed methodology are demonstrated
through a series of experiments using real data.Comment: arXiv admin note: text overlap with arXiv:1507.0251