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Non-Line-of-Sight (NLOS) imaging reconstructs occluded scenes based on indirect diffuse reflections. The computational complexity and memory consumption of existing NLOS reconstruction algorithms make them challenging to be implemented in real-time. This paper presents a fast and memory-efficient phasor field-diffraction-based NLOS reconstruction algorithm. In the proposed algorithm, the radial property of the Rayleigh Sommerfeld diffraction (RSD) kernels along with the linear property of Fourier transform are utilized to reconstruct the Fourier domain representations of RSD kernels using a set of kernel bases. Moreover, memory consumption is further reduced by sampling the kernel bases in a radius direction and constructing them during the run-time. According to the analysis, the memory efficiency can be improved by as much as $\mathbf {220}\times$ 220 × . Experimental results show that compared with the original RSD algorithm, the reconstruction time of the proposed algorithm is significantly reduced with little impact on the final imaging quality. [ABSTRACT FROM AUTHOR]

We present the design and characterization of a fully-integrated array of $16 \times 16$ Single-Photon Avalanche Diodes (SPADs) with fast-gating capabilities and 16 on-chip 6 ps time-to-digital converters, which has been embedded in a compact imaging module. Such sensor has been developed for Non-Line-Of-Sight imaging applications, which require: i) a narrow instrument response function, for a centimeteraccurate single-shot precision; ii) fast-gated SPADs, for time-filtering of directly reflected photons; iii) high photon detection probability, for acquiring faint signals undergoing multiple scattering events. Thanks to a novel multiple differential SPAD-SPAD sensing approach, SPAD detectors can be swiftly activated in less than 500 ps and the full-width at half maximum of the instrument response function is always less than 75 ps (60 ps on average). Temporal responses are consistently uniform throughout the gate window, showing just few picoseconds of time dispersion when 30 ns gate pulses are applied, while the differential non-linearity is as low as 250 fs. With a photon detection probability peak of 70% at 490 nm, a fill-factor of 9.6% and up to $1.6 \cdot 10^{8}$ photon time-tagging measurements per second, such sensor fulfills the demand for fully-integrated imaging solutions optimized for non-line-of-sight imaging applications, enabling to cut exposure times while also optimizing size, weight, power and cost, thus paving the way for further scaled architectures. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and inverse light transport theory have resulted in several non-line-of-sight imaging techniques. These techniques use photon time-of-flight information encoded in light after multiple, diffuse reflections to reconstruct a three-dimensional scene. In this paper, we propose and describe two iterative backprojection algorithms, the additive error backprojection (AEB) and multiplicative error backprojection (MEB), whose goal is to improve the reconstruction of the scene under investigation over non-iterative backprojection algorithms. We evaluate the proposed algorithms' performance applied to simulated and real data (gathered from an experimental setup where the system needs to reconstruct an unknown scene). Results show that the proposed iterative algorithms are able to provide better reconstruction than the unfiltered, non-iterative backprojection algorithm for both simulated and physical scenes, but are more sensitive to errors in the light transport model. [ABSTRACT FROM AUTHOR]