Non-line-of-sight imaging has attracted more attentions for its wide applications. Even though ultrasensitive cameras/detectors with high time-resolution are available, current back-projection methods are still powerless to acquire a satisfying reconstruction of multiple hidden objects due to severe aliasing artifacts. Here, a novel back-projection method is developed to reconstruct multiple hidden objects. Our method considers decomposing all the ellipsoids in a confidence map into several "clusters" belonging to different objects (namely "ellipsoid mode decomposition"), and then reconstructing the objects individually from their ellipsoid modes by filtering and thresholding, respectively. Importantly, the simulated and experimental results demonstrate that this method can effectively eliminate the impacts of aliasing artifacts and exhibits potential advantages in separating, locating and recovering multiple hidden objects, which might be a good base for reconstructing complex non-line-of-sight scenes.

An effective approach for general measurement based on weak value amplification using non-Gaussian broadband light sources is demonstrated. This scheme can reduce the difficulty of preparing the measurement apparatus and correcting the systematic error caused by the imperfection of device's wave function, thus making the weak-value amplification scheme more convenient and robust for practical field applications. The influence of several common noise sources in a general application based on weak value amplification is analyzed theoretically and examined experimentally. A purely imaginary weak-value phase measurement system is considered for the noise model verification experimentally. In this we show how to optimize the precision of measurement in a unique solution that takes into account the interplay between precision and uncertainty, and explicate the ramifications of such a compromising and pragmatic approach.

Photon counting Lidar applied to long-distance ranging will face the problem of the number of received photons fluctuating with target distance and reflectivity. The Poisson probability model indicates that the range walk error of the system fluctuates with the number of received photons, which deteriorates the performance of ranging. We propose a novel approach based on the probability distribution regulator with feedback mechanism to achieve small and stable range walk error by actively controlling the number of photons incident on the detector. In this method, the attenuation rate of the probability distribution regulator varies with the number of photons to ensure that the number of photons incident on the detector is a fixed value. The simulated and experimental results with the approach demonstrate that ranging accuracy is improved significantly, and the stability of the range walk error is guaranteed.

Photon-counting LiDAR using a two-dimensional (2D) array detector has the advantages of high lateral resolution and fast acquisition speed. The non-uniform intensity profile of the illumination beam and non-uniform quantum efficiency of the detectors in the 2D array deteriorate the imaging quality. Herein, we propose a photon-counting LiDAR system that uses a spatial light modulator to control the spatial intensity to compensate for both the non-uniform intensity profile of the illumination beam, and the variation in the quantum efficiency of the detectors in the 2D array. Using a 635 nm peak wavelength and 4 mW average power semiconductor laser, lab-based experiments at a 4.27 m stand-off distance were performed to verify the effectiveness of the proposed method. Compared with the unmodulated method, the standard deviation of the intensity image of the proposed method is reduced from 0.109 to 0.089 for a whiteboard target, with an average signal photon number of 0.006 per pixel.

Photon counting lidar for long-range detection faces the problem of declining ranging performance caused by background noise. Current anti-noise methods are not robust enough in the case of weak signal and strong background noise, resulting in poor ranging error. In this work, based on the characteristics of the uncertainty of echo signal and noise in photon counting lidar, an entropy-based anti-noise method is proposed to reduce the ranging error under high background noise. Firstly, the photon counting entropy, which is considered as the feature to distinguish signal from noise, is defined to quantify the uncertainty of fluctuation among photon events responding to the Geiger mode avalanche photodiode. Then, the photon counting entropy is combined with a windowing operation to enhance the difference between signal and noise, so as to mitigate the effect of background noise and estimate the time of flight of the laser pulses. Simulation and experimental analysis show that the proposed method improves the anti-noise performance well, and experimental results demonstrate that the proposed method effectively mitigates the effect of background noise to reduce ranging error despite high background noise.