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Alignment of a single-pixel quantum ghost imaging setup is complex and requires extreme precision. Due to misalignment, easily created by human error in the alignment process, reconstructed images are often translated off the central imaging axis. This becomes problematic for intelligent object detection and identification in fast imaging cases, as these algorithms are unable to achieve early image identification. Here, we implemented a U-net algorithm to correctly recognize images in the early reconstruction stage regardless of any off-axis translation. The U-net was trained on a uniquely curated blurred, noised, and off-axis translated dataset. We achieved a 5× reduction in imaging speeds by early image identification in four different translation directions.

This paper proposes a Bayesian approach to enable single photon avalanche diode (SPAD) arrays to be used as pseudo event cameras that report changes in the scene. Motivated by the working principle of event cameras, which produce sparse events associated with light flux changes, we adopt a changepoint detection strategy to generate intensity and depth change event streams from direct time-of-flight (dToF) sequences measured by SPAD arrays. Although not our main goal, the algorithm also produces as a by-product, intensity and depth estimates. Unlike the output of passive event cameras that only correspond to light flux changes, the change events detected from the sequential dToFs can relate to changes in light flux and/or depth. The integration of the proposed Bayesian approach with single-photon LiDAR (SPL) systems provides a novel solution to achieve active neuromorphic 3D imaging that offers the advantages of significantly reduced output redundancy and in particular the capacity to report scene depth changes. For each pixel of the SPAD array, asynchronous events are generated by performing online Bayesian inference to detect changepoints and estimate the model parameters simultaneously from individual single-photon measurements. Experiments are conducted on synthetic data and real dToF measurements acquired by a 172×126 pixel SPAD camera to demonstrate the feasibility and efficiency of the proposed Bayesian approach.

Spatially structured optical modes exhibit a group velocity lower than c, resulting in a measurable temporal delay with respect to plane waves. Here, we develop a technique to image this temporal delay and measure it across a set of optical modes. An inevitable consequence of spatially varying delay is temporal broadening of the mode. As such, for a focused Gaussian, we observe an ≈ 1 % increase in the temporal profile, corresponding to a narrowing of the optical spectrum by ≈ 0.03 nm. This work shows that imaging is essential to fully understanding the changes to the group velocity for structured modes.

Single-Photon Avalanche Diode (SPAD) direct Time-of-Flight (dToF) sensors provide depth imaging over long distances, enabling the detection of objects even in the absence of contrast in colour or texture. However, distant objects are represented by just a few pixels and are subject to noise from solar interference, limiting the applicability of existing computer vision techniques for high-level scene interpretation. We present a new SPAD-based vision system for human activity recognition, based on convolutional and recurrent neural networks, which is trained entirely on synthetic data. In tests using real data from a 64×32 pixel SPAD, captured over a distance of 40 m, the scheme successfully overcomes the limited transverse resolution (in which human limbs are approximately one pixel across), achieving an average accuracy of 89% in distinguishing between seven different activities. The approach analyses continuous streams of video-rate depth data at a maximal rate of 66 FPS when executed on a GPU, making it well-suited for real-time applications such as surveillance or situational awareness in autonomous systems.

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 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 algorithms.

3D time-of-flight (ToF) image sensors are used widely in applications such as self-driving cars, augmented reality (AR), and robotics. When implemented with single-photon avalanche diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to be small, leading to low lateral resolution, which combined with low signal-to-background ratio (SBR) levels under high ambient illumination, may lead to difficulties in scene interpretation. In this paper, we use synthetic depth sequences to train a 3D convolutional neural network (CNN) for denoising and upscaling (×4) depth data. Experimental results, based on synthetic as well as real ToF data, are used to demonstrate the effectiveness of the scheme. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance.

The precise measurement of a target depth has applications in biophysics and nanophysics, and non-linear optical methods are sensitive to intensity changes on very small length scales. By exploiting the high sensitivity of an autocorrelator's dependency on path length, we propose a technique that achieves ≈30 nm depth precision for each pixel in 30 seconds. Our method images up-converted pulses from a non-linear crystal using a sCMOS (scientific Complementary Metal-Oxide-Semiconductor) camera and converts the intensity recorded by each pixel to a delay. By utilising statistical estimation theory and using the data from a set of 32×32 pixels, the standard error (SE) of the detected delay falls below 1 nm after 30 seconds of measurement. Numerical simulations show that this result is extremely close to what can be achieved with a shot-noise-limited source and is consistent with the precision that can be achieved with a sCMOS camera.

3D time-of-flight (ToF) imaging is used in a variety of applications such as augmented reality (AR), computer interfaces, robotics and autonomous systems. Single-photon avalanche diodes (SPADs) are one of the enabling technologies providing accurate depth data even over long ranges. By developing SPADs in array format with integrated processing combined with pulsed, flood-type illumination, high-speed 3D capture is possible. However, array sizes tend to be relatively small, limiting the lateral resolution of the resulting depth maps and, consequently, the information that can be extracted from the image for applications such as object detection. In this paper, we demonstrate that these limitations can be overcome through the use of convolutional neural networks (CNNs) for high-performance object detection. We present outdoor results from a portable SPAD camera system that outputs 16-bin photon timing histograms with 64×32 spatial resolution, with each histogram containing thousands of photons. The results, obtained with exposure times down to 2 ms (equivalent to 500 FPS) and in signal-to-background (SBR) ratios as low as 0.05, point to the advantages of providing the CNN with full histogram data rather than point clouds alone. Alternatively, a combination of point cloud and active intensity data may be used as input, for a similar level of performance. In either case, the GPU-accelerated processing time is less than 1 ms per frame, leading to an overall latency (image acquisition plus processing) in the millisecond range, making the results relevant for safety-critical computer vision applications which would benefit from faster than human reaction times.

Light-in-flight (LIF) imaging is the measurement and reconstruction of light's path as it moves and interacts with objects. It is well known that relativistic effects can result in apparent velocities that differ significantly from the speed of light. However, less well known is that Rayleigh scattering and the effects of imaging optics can lead to observed intensities changing by several orders of magnitude along light's path. We develop a model that enables us to correct for all of these effects, thus we can accurately invert the observed data and reconstruct the true intensity-corrected optical path of a laser pulse as it travels in air. We demonstrate the validity of our model by observing the photon arrival time and intensity distribution obtained from single-photon avalanche detector (SPAD) array data for a laser pulse propagating towards and away from the camera. We can then reconstruct the true intensity-corrected path of the light in four dimensions (three spatial dimensions and time).

The number of applications that use depth imaging is increasing rapidly, e.g. self-driving autonomous vehicles and auto-focus assist on smartphone cameras. Light detection and ranging (LIDAR) via single-photon sensitive detector (SPAD) arrays is an emerging technology that enables the acquisition of depth images at high frame rates. However, the spatial resolution of this technology is typically low in comparison to the intensity images recorded by conventional cameras. To increase the native resolution of depth images from a SPAD camera, we develop a deep network built to take advantage of the multiple features that can be extracted from a camera's histogram data. The network is designed for a SPAD camera operating in a dual-mode such that it captures alternate low resolution depth and high resolution intensity images at high frame rates, thus the system does not require any additional sensor to provide intensity images. The network then uses the intensity images and multiple features extracted from down-sampled histograms to guide the up-sampling of the depth. Our network provides significant image resolution enhancement and image denoising across a wide range of signal-to-noise ratios and photon levels. Additionally, we show that the network can be applied to other data types of SPAD data, demonstrating the generality of the algorithm.

We provide a statistically robust and accurate framework to measure and track the polarisation state of light employing Hong-Ou-Mandel interference. This is achieved by combining the concepts of maximum likelihood estimation and Fisher information applied to photon detection events. Such an approach ensures that the Cramér-Rao bound is saturated and changes to the polarisation state are established in an optimal manner. Using this method, we show that changes in the linear polarisation state can be measured with 0.6 arcminute precision (0.01 degrees).

A remote-sensing system that can determine the position of hidden objects has applications in many critical real-life scenarios, such as search and rescue missions and safe autonomous driving. Previous work has shown the ability to range and image objects hidden from the direct line of sight, employing advanced optical imaging technologies aimed at small objects at short range. In this work we demonstrate a long-range tracking system based on single laser illumination and single-pixel single-photon detection. This enables us to track one or more people hidden from view at a stand-off distance of over 50 m. These results pave the way towards next generation LiDAR systems that will reconstruct not only the direct-view scene but also the main elements hidden behind walls or corners.

We experimentally demonstrate the feasibility of a super-resolution technique based on eigenmode decomposition. This technique has been proposed theoretically but, to the best of our knowledge, has not previously been realized experimentally for optical imaging systems with circular apertures. We use a standard diffraction-limited 4f imaging system with circular apertures for which the radial eigenmodes are the circular prolate spheroidal functions. For three original objects with different content of angular information we achieve 45%, 49%, and 89% improvement of resolution over the Rayleigh limit. The work presented can be considered as progress towards the goal of reaching the quantum limits of super-resolution.

Orbital angular momentum (OAM) entanglement is investigated in the Bessel-Gaussian (BG) basis. Having a readily adjustable radial scale, BG modes provide an alternative basis for OAM entanglement over Laguerre-Gaussian modes. We show that the OAM bandwidth in terms of BG modes can be increased by selection of particular radial wavevectors and leads to a flattening of the spectrum, which allows for higher dimensionality in the entangled state. We demonstrate entanglement in terms of BG modes by performing a Bell-type experiment and showing a violation of the Clauser-Horne-Shimony-Holt inequality for the ℓ = ±1 subspace. In addition, we use quantum state tomography to indicate higher-dimensional entanglement in terms of BG modes.

We describe an experimental implementation of a free-space 11-dimensional communication system using orbital angular momentum (OAM) modes. This system has a maximum measured OAM channel capacity of 2.12 bits/photon. The effects of Kolmogorov thin-phase turbulence on the OAM channel capacity are quantified. We find that increasing the turbulence leads to a degradation of the channel capacity. We are able to mitigate the effects of turbulence by increasing the spacing between detected OAM modes. This study has implications for high-dimensional quantum key distribution (QKD) systems. We describe the sort of QKD system that could be built using our current technology.

The effect of atmosphere turbulence on light's spatial structure compromises the information capacity of photons carrying the Orbital Angular Momentum (OAM) in free-space optical (FSO) communications. In this paper, we study two aberration correction methods to mitigate this effect. The first one is the Shack-Hartmann wavefront correction method, which is based on the Zernike polynomials, and the second is a phase correction method specific to OAM states. Our numerical results show that the phase correction method for OAM states outperforms the Shark-Hartmann wavefront correction method, although both methods improve significantly purity of a single OAM state and the channel capacities of FSO communication link. At the same time, our experimental results show that the values of participation functions go down at the phase correction method for OAM states, i.e., the correction method ameliorates effectively the bad effect of atmosphere turbulence.

A 1 x 8 fiber array is used as the front-end of a receiver system. Each channel has a different length of fiber, resulting in each channel signal arriving at the detector at a pre-determined interval relative to a constant repetitive frequency signal. We demonstrate that these eight channels can be efficiently coupled to an individual single-photon detector such that the arrival-time of a photon in each is distinguishable from the next. Thus, we demonstrate spatial position to time information exchange, resulting in a photon-counting array using a single detector. The receiver system could be implemented in numerous applications, including time-resolved photoluminescence, low-light level spectroscopy and quantum information processing.

UV illumination of a lithium niobate Q-switch was demonstrated as an effective means to eliminate a loss in hold-off and associated prelasing that occurs under cold temperature operation of Q-switched lasers. This degradation occurs due to the pyroelectric effect, where an accumulation of charge on crystal faces results in a reduction in the Q-switch hold-off and a spatially variable loss of the Q-switch in its high-transmission state, both resulting in lowering of the maximum Q-switched pulse energy. With UV illumination, the resulting creation of photo-generated carriers was shown to be effective in eliminating both of these effects. A Q-switched Nd:YAG laser utilizing UV-illuminated LiNbO(3) was shown to operate under cold temperatures without prelasing or spatially variable loss.

We present a system which uses a single spatial light modulator to control the spin angular momentum of multiple optical traps. These traps may be independently controlled both in terms of spatial location and in terms of their spin angular momentum content. The system relies on a spatial light modulator used in a "split-screen" configuration to generate beams of orthogonal polarisation states which are subsequently combined at a polarising beam splitter. Defining the phase difference between the beams with the spatial light modulator enables control of the polarisation state of the light. We demonstrate the functionality of the system by controlling the rotation and orientation of birefringent vaterite crystals within holographic optical tweezers.

We assess the performance of a CMOS camera for the measurement of particle position within optical tweezers and the associated autocorrelation function and power spectrum. Measurement of the displacement of the particle from the trap center can also be related to the applied force. By considering the Allan variance of these measurements, we show that such cameras are capable of reaching the thermal limits of nanometer and femtonewton accuracies, and hence are suitable for many of the applications that traditionally use quadrant photodiodes. As an example of a multi-particle measurement we show the hydrodynamic coupling between two particles.

Beams with helical phasefronts described by exp(ilphi) carry an orbital angular momentum equivalent to lh per photon. Using diffractive optics this helical phase structure can be applied to every spectral component of the beam such that a spatially coherent white-light beam can carry orbital angular momentum without any chromatic distortion. This achromatic property can be hard to achieve in spin angular momentum where pure circular polarization is difficult to maintain across a finite spectral bandwidth. We illustrate the achromatic, helical phase structure of a white-light beam by observing the transfer of its orbital angular momentum to particles held in optical tweezers.

We have developed a real time interface for holographic optical tweezers where the operator's fingertips are mapped to the positions of silica beads captured in optical traps. The beads act as the fingertips of a microhand which can be used to manipulate objects that otherwise do not lend themselves to tweezers control, e.g. objects that are strongly scattering or highly light sensitive. We illustrate the use of the microhand for the real time manipulation of micron sized chrome particles.

We measure the local skew angle of the Poynting vector within a helically-phased, exp (il phi), beam using a Shack Hartmann wavefront sensor. It is the skew angle of the Poynting vector with respect to the beam axis that gives rise to the orbital angular momentum of a light beam. We confirm that this skew angle is l/kr, corresponding to an orbital angular momentum of l? per photon. Measurement of orbital angular momentum in this way is an alternative to interferometric techniques giving a non-ambiguous result to both the magnitude and sign of l from a single measurement, without any restriction on the optical bandwidth.

We report the creation of white-light, achromatic Bessel beams using a spatial light modulator and a prism to compensate for the dispersion. Unlike the Bessel beam created by a refractive axicon, this achromatic beam has a radial wavevector and hence an intensity cross-section which is independent of wavelength. The technique also lends itself to the generation of higher order Bessel beams with an on-axis optical vortex and associated orbital angular momentum.

Holographic or diffractive optical components are widely implemented using spatial light modulators within optical tweezers to form multiple, and/or modified traps. We show that by further modifying the hologram design to account for residual aberrations, the fidelity of the focused beams can be significantly improved, quantified by a spot sharpness metric. However, the impact this improvement has on the quality of the optical trap depends upon the particle size. For particle diameters on the order of 1 microm, aberration correction can improve the trap performance metric, which is the ratio of the mean square displacement of a corrected trap to an uncorrected trap, in excess of 25%, but for larger particles the trap performance is not unduly affected by the aberrations typically encountered in commercial spatial light modulators.

Phase-hologram patterns that can shape the intensity distribution of a light beam in several planes simultaneously can be calculated with an iterative Gerchberg-Saxton algorithm [T. Haist et al., Opt. Commun. 140, 299 (1997)]. We apply this algorithm in holographic optical tweezers. This allows us to simultaneously trap several objects in individually controllable arbitrary 3-dimensional positions. We demonstrate the interactive use of our approach by trapping microscopic spheres and moving them into an arbitrary 3-dimensional configuration.

We have developed holographic optical tweezers that can manipulate many particles simultaneously in three dimensions in order to create micro-crystal structures that extend over many tens of microns. The technique uses specific hologram-design algorithms to create structures that can be dynamically scaled or rotated about arbitrary axes. We believe the generation and control of pre-determined crystal-like structures have significant potential in fields as diverse as photonic-crystal construction, seeding of biological tissue growth and creation of metrological standards within nanotechnology.