We present what we believe to be a novel method based on pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy for salient targets detection. In the foundation of studying the statistical property of Rényi entropy via PWVD, the residual entropy-based saliency map of an input image can be obtained. From the saliency map, target detection is completed by the simple and convenient threshold segmentation. Experimental results demonstrate the proposed method can detect targets effectively in complex ground scenes.

In this paper, we propose a protocol for angular displacement estimation based upon orbital angular momentum coherent state and a SU(1,1)-SU(2) hybrid interferometer. This interferometer consists of an optical parametric amplifier, a beam splitter and reflection mirrors, hereon we use a quantum detection strategy −− balanced homodyne detection. The results indicate that superresolution and super-sensitivity can be realized with ideal condition. Additionally, we study the impact of photon loss on the resolution and the sensitivity, and the robustness of our protocol is also discussed. Finally, we demonstrate the advantage of our protocol over SU(1,1) and summarize the merits of orbital angular momentum-enhanced protocol.

We present a three-dimensional imaging method using a pulsed laser as a flood illuminating source and an intensified camera as the receiver with exponentially modulated gain. The depth map of a scene is obtained from two intensity images and the depth accuracy is independent of the depth of the target in the scene. We demonstrate a depth-independent depth accuracy of 0.32 m in an indoor experiment and obtain a depth map of an outdoor scene ranging from 150 to 180 m under a lower signal to noise ratio condition.

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.

We present a technique to reconstruct a higher resolution of depth map of range gating imaging lidar by applying the delays of the gates to a typical range gating lidar system during the detection of each returned laser pulse with the encoding of the returned signal. With the consequent delays of the gate, the depth of the scene is extended accordingly. A multipulse gate-delayed range gating lidar system is designed to prove the resolution improvement from 6 to 1.5 m. The unchanged peak power of the laser, the widths of the laser pulse and the sampling period result in a simple structure of the lidar system.