The ability to detect motion and track a moving object hidden around a corner or behind a wall provides a crucial advantage when physically going around the obstacle is impossible or dangerous. Previous methods have demonstrated that is possible to reconstruct the shape of an object hidden from view. However, these methods do not enable the tracking of movement in real-time. We demonstrate a compact non-line-of-sight laser ranging technology that relies upon the ability to send light around an obstacle using a scattering floor and to detect the return signal from a hidden object with only a few seconds acquisition time. By detecting this signal with a single-photon avalanche diode (SPAD) camera, we follow the movement of an object located a meter away from the camera with centimetre precision. We discuss the possibility of applying this technology to a variety of real-life situations in the 1 near future.Recent years have seen remarkable advances in the field of image processing and data acquisition, allowing for a range of novel applications [1][2][3][4][5][6][7][8]. An exciting new avenue is using optical imaging techniques to observe and track objects that are both in movement and hidden from the direct line-of-sight. The ability to detect motion and track a moving object hidden from view would provide a crucial advantage when physically going around the obstacle is impossible or dangerous, for example to detect a person moving behind a wall or a car approaching from behind a blind corner.Techniques for imaging static objects that are hidden from view have been recently demonstrated relying on, for example, radar technology [9,10], variations of laser illuminated detection and ranging (LIDAR) [3,5,11,12], or speckle-based imaging. The latter approach was first developed for imaging through opaque barriers [13][14][15], and also allows for imaging around corners [16,17]. The work of Velten et al. [5] and, more recently, Buttafava et al. [8] sets out to establish the 3D shape of a static hidden object by collecting the return scattered light with a streak camera or single-photon avalanche diode, respectively. While remarkable 3D reconstruction of objects are achieved with these techniques, Buttafava et al. point out that the requirement for scanning and subsequent long acquisition times mean that their technique is currently unsuitable for imaging moving objects.Notwithstanding these ingenious imaging systems, locating the position of a hidden object in motion and monitoring its movement in real time remains to date a major challenge. We set out to solve the tracking problem and develop a technique based on both hardware and software implementations that are specifically designed for this 2 purpose. Our solution is based on a LIDAR-like approach where a single-photon avalanche diode (SPAD) camera [7,[18][19][20][21][22] is used to image light that is backscattered from beyond the direct line-of-sight (see Methods for camera details). The high temporal resolution of the camera relies on the fact that each indiv...

The ability to detect motion and track a moving object hidden around a corner or behind a wall provides a crucial advantage when physically going around the obstacle is impossible or dangerous. Previous methods have demonstrated that is possible to reconstruct the shape of an object hidden from view. However, these methods do not enable the tracking of movement in real-time. We demonstrate a compact non-line-of-sight laser ranging technology that relies upon the ability to send light around an obstacle using a scattering floor and to detect the return signal from a hidden object with only a few seconds acquisition time. By detecting this signal with a single-photon avalanche diode (SPAD) camera, we follow the movement of an object located a meter away from the camera with centimetre precision. We discuss the possibility of applying this technology to a variety of real-life situations in the 1 near future.Recent years have seen remarkable advances in the field of image processing and data acquisition, allowing for a range of novel applications [1][2][3][4][5][6][7][8]. An exciting new avenue is using optical imaging techniques to observe and track objects that are both in movement and hidden from the direct line-of-sight. The ability to detect motion and track a moving object hidden from view would provide a crucial advantage when physically going around the obstacle is impossible or dangerous, for example to detect a person moving behind a wall or a car approaching from behind a blind corner.Techniques for imaging static objects that are hidden from view have been recently demonstrated relying on, for example, radar technology [9,10], variations of laser illuminated detection and ranging (LIDAR) [3,5,11,12], or speckle-based imaging. The latter approach was first developed for imaging through opaque barriers [13][14][15], and also allows for imaging around corners [16,17]. The work of Velten et al. [5] and, more recently, Buttafava et al. [8] sets out to establish the 3D shape of a static hidden object by collecting the return scattered light with a streak camera or single-photon avalanche diode, respectively. While remarkable 3D reconstruction of objects are achieved with these techniques, Buttafava et al. point out that the requirement for scanning and subsequent long acquisition times mean that their technique is currently unsuitable for imaging moving objects.Notwithstanding these ingenious imaging systems, locating the position of a hidden object in motion and monitoring its movement in real time remains to date a major challenge. We set out to solve the tracking problem and develop a technique based on both hardware and software implementations that are specifically designed for this 2 purpose. Our solution is based on a LIDAR-like approach where a single-photon avalanche diode (SPAD) camera [7,[18][19][20][21][22] is used to image light that is backscattered from beyond the direct line-of-sight (see Methods for camera details). The high temporal resolution of the camera relies on the fact that each indiv...

Imaging through a strongly diffusive medium remains an outstanding challenge in particular in association with applications in biological and medical imaging. Here we propose a method based on a single-photon time-of-flight camera that allows, in combination with computational processing of the spatial and full temporal photon distribution data, to image an object embedded inside a strongly diffusive medium over more than 80 transport mean free paths. The technique is contactless and requires one second acquisition times thus allowing Hz frame rate imaging. The imaging depth corresponds to several cm of human tissue and allows one to perform deep-body imaging, here demonstrated as a proof-of-principle.

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Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems.

Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial structure in the detector or illumination, i.e., imaging with just a single-point sensor, would require solving a very strongly ill-posed inverse retrieval problem that to date has not been solved. Here, we demonstrate a data-driven approach in which full 3D information is obtained with just a single-point, single-photon avalanche diode that records the arrival time of photons reflected from a scene that is illuminated with short pulses of light. Imaging with single-point time-of-flight (temporal) data opens new routes in terms of speed, size, and functionality. As an example, we show how the training based on an optical time-of-flight camera enables a compact radio-frequency impulse radio detection and ranging transceiver to provide 3D images. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be trained, which in imaging applications implicates prohibitively expensive collections with specific imaging instruments. This paper introduces a novel framework to train variational inference for inverse problems exploiting in combination few experimentally collected data, domain expertise and existing image data sets. In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts. Extensive simulated experiments show the advantages of the proposed framework. The approach is then applied to two real experimental optics settings: holographic image reconstruction and imaging through highly scattering media. In both settings, state of the art reconstructions are achieved with little collection of training data.