Fundamental limits to depth imaging with single-photon detector array sensors

Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time-photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.

Human activity recognition using a single-photon direct time-of-flight sensor

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.

Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media

The advancement of imaging systems has significantly ameliorated various technologies, including Intelligence Surveillance Reconnaissance Systems and Guidance Systems, by enhancing target detection, recognition, identification, positioning, and tracking capabilities. These systems can be countered by deploying obscurants like smoke, dust, or fog to hinder visibility and communication. However, these counter-systems affect the visibility of both sides of the cloud. In this sense, this manuscript introduces a new concept of a smoke cloud composed of engineered Janus particles to conceal the target image on one side while providing clear vision from the other. The proposed method exploits the unique scattering properties of Janus particles, which selectively interact with photons from different directions to open up the possibility of asymmetric imaging. This approach employs a model that combines a genetic algorithm with Discrete Dipole Approximation to optimize the Janus particles' geometrical parameters for the desired scattering properties. Moreover, we propose a Monte Carlo-based approach to calculate the image formed as photons pass through the cloud, considering highly asymmetric particles, such as Janus particles. The effectiveness of the cloud in disguising a target is evaluated by calculating the Probability of Detection (PD) and the Probability of Identification (PID) based on the constructed image. The optimized Janus particles can produce a cloud where it is possible to identify a target more than 50% of the time from one side (PID > 50%) while the target is not detected more than 50% of the time from the other side (PD < 50%). The results demonstrate that the Janus particle-engineered smoke enables asymmetric imaging with simultaneous concealment from one side and clear visualization from the other. This research opens intriguing possibilities for modern obscurant design and imaging systems through highly asymmetric and inhomogeneous particles besides target detection and identification capabilities in challenging environments.

Rapid single-photon color imaging of moving objects

This paper outlines an experimental demonstration of a Bayesian image reconstruction approach to achieve rapid single-photon color imaging of moving objects. The capacity to extract the color of objects is important in a variety of target identification and computer vision applications. Nonetheless, it remains challenging to achieve high-speed color imaging of moving objects in low-photon flux environments. The low-photon regime presents particular challenges for efficient spectral separation and identification, while unsupervised image reconstruction algorithms are often slow and computationally expensive. In this paper, we address both of these difficulties using a combination of hardware and computational solutions. We demonstrate color imaging using a Single-Photon Avalanche Diode (SPAD) detector array for rapid, low-light-level data acquisition, with an integrated color filter array (CFA) for efficient spectral unmixing. High-speed image reconstruction is achieved using a bespoke Bayesian algorithm to produce high-fidelity color videos. The analysis is conducted first on simulated data allowing different pixel formats and photon flux scenarios to be investigated. Experiments are then performed using a plasmonic metasurface-based CFA, integrated with a 64 × 64 pixel format SPAD array. Passive imaging is conducted using white-light illumination of multi-colored, moving targets. Intensity information is recorded in a series of 2D photon-counting SPAD frames, from which accurate color information is extracted using the fast Bayesian method introduced herein. The per-frame reconstruction rate proves to be hundreds of times faster than the previous computational method. Furthermore, this approach yields additional information in the form of uncertainty measures, which can be used to assist with imaging system optimization and decision-making in real-world applications. The techniques demonstrated point the way towards rapid video-rate single-photon color imaging. The developed Bayesian algorithm, along with more advanced SPAD technology and utilization of time-correlated single-photon counting (TCSPC) will permit live 3D, color videography in extremely low-photon flux environments.

Visualising varnish removal for conservation of paintings by fluorescence lifetime imaging (FLIM)

The removal of varnish from the surface is a key step in painting conservation. Varnish removal is traditionally monitored by examining the painting surface under ultraviolet illumination. We show here that by imaging the fluorescence lifetime instead, much better contrast, sensitivity, and specificity can be achieved. For this purpose, we developed a lightweight (4.8 kg) portable instrument for macroscopic fluorescence lifetime imaging (FLIM). It is based on a time-correlated single-photon avalanche diode (SPAD) camera to acquire the FLIM images and a pulsed 440 nm diode laser to excite the varnish fluorescence. A historical model painting was examined to demonstrate the capabilities of the system. We found that the FLIM images provided information on the distribution of the varnish on the painting surface with greater sensitivity, specificity, and contrast compared to the traditional ultraviolet illumination photography. The distribution of the varnish and other painting materials was assessed using FLIM during and after varnish removal with different solvent application methods. Monitoring of the varnish removal process between successive solvent applications by a swab revealed an evolving image contrast as a function of the cleaning progress. FLIM of dammar and mastic resin varnishes identified characteristic changes to their fluorescence lifetimes depending on their ageing conditions. Thus, FLIM has a potential to become a powerful and versatile tool to visualise varnish removal from paintings.

Graphical Abstract