Enhancing LiDAR performance using threshold photon-number-resolving detection.

Single-photon light detection and ranging (LiDAR) is widely used to reconstruct 3D scenes. Nevertheless, depth and reflectivity maps obtained by single-photon detection usually suffer from noise problems. Threshold LiDAR techniques using photon-number-resolving detectors were proposed to suppress noise by filtering low photon numbers, but these techniques renounce multiple levels of information and could not be compatible when it comes to high-noise low-signal regime. In this manuscript, we propose a detection scheme which combines the noise suppression of threshold detection with the signal amplification of photon-number-resolving detectors to further enhance LiDAR performance. The enhancement attained is compared to single-photon and threshold detection schemes under a wide range of signal and noise conditions, in terms of signal-to-noise-ratio (SNR), detection rate and false alarm rate, which are key metrics for LiDAR. Extensive simulations and real-world experiments show that the proposed scheme can reconstruct better depth and reflectivity maps. These results enable the development of high-efficient and low-noise LiDAR systems.

Extended depth of field for Fresnel zone aperture camera via fast passive depth estimation.

The lensless camera with incoherent illumination has gained significant research interest for its thin and flexible structure. However, it faces challenges in resolving scenes with a wide depth of field (DoF) due to its depth-dependent point spread function (PSF). In this paper, we present a single-shot method for extending the DoF in Fresnel zone aperture (FZA) cameras at visible wavelengths through passive depth estimation. The improved ternary search method is utilized to determine the depth of targets rapidly by evaluating the sharpness of the back propagation reconstruction. Based on the depth estimation results, a set of reconstructed images focused on targets at varying depths are derived from the encoded image. After that, the DoF is extended through focus stacking. The experimental results demonstrate an 8-fold increase compared with the calibrated DoF at 130 mm depth. Moreover, our depth estimation method is five times faster than the traversal method, while maintaining the same level of accuracy. The proposed method facilitates the development of lensless imaging in practical applications such as photography, microscopy, and surveillance.

Short-wavelength-infrared upconversion edge enhancement imaging based on a Laguerre-Gaussian composite vortex filter.

Edge-enhanced imaging by spiral phase contrast has proven instrumental in revealing phase or amplitude gradients of an object, with notable applications spanning feature extraction, target recognition, and biomedical fields. However, systems deploying spiral phase plates encounter limitations in phase mask modulation, hindering the characterization of the modulation function during image reconstruction. To address this need, we propose and demonstrate an innovative nonlinear reconstruction method using a Laguerre-Gaussian composite vortex filter, which modulates the spectrum of the target. The involved nonlinear process spectrally transforms the incident short-wavelength-infrared (SWIR) signal from 1550 to 864 nm, subsequently captured by a silicon charge-coupled device. Compared with conventional schemes, our novel filtering method effectively suppresses the diffraction noise, significantly enhancing image contrast and resolution. By loading specific phase holograms on the spatial light modulator, bright-field imaging, isotropic, amplitude-controlled anisotropic, and directional second-order edge-enhanced imaging are realized. Anticipated applications for the proposed SWIR edge-enhanced imaging system encompass domains such as artificial intelligence recognition, deep tissue medical diagnostics, and non-destructive defect inspection. These applications underscore the valuable potential of our cutting-edge methodology in furthering both scientific exploration and practical implementations.

Dual-insensitive zero refraction based on annular photonic crystals.

Over the past few years, zero refraction (ZR) represents an important breakthrough in field of distinctive dynamic optical beam tuning for double-zero-index (media with zero relative permittivity and permeability) photonic crystals (PCs). Because accidental ZR effects for conventional PCs is sensitive to the structural or refractive index parameters, how to enhance the robustness of ZR design is vital for applications. Here, we report a pioneering wave property that reveals a non-accidental Dirac-like cone dispersion composed of low- or high-energy bands, corresponding to dual-insensitive ZR effects for both transverse magnetic and transverse electric polarizations based on the same annular PCs. The performance of non-accidental ZR is simultaneously based on refractive index-insensitivity and structural- insensitivity, attributed to a satisfactory filling ratio.

Photon-efficient 3D reconstruction employing a edge enhancement method.

Photon-efficient 3D reconstruction under sparse photon conditions remains challenges. Especially for scene edge locations, the light scattering results in a weaker echo signal than non-edge locations. Depth images can be viewed as smooth regions stitched together by edge segmentation, yet none of the existing methods focus on how to improve the accuracy of edge reconstruction when performing 3D reconstruction. Moreover, the impact of edge reconstruction to overall depth reconstruction hasn't been investigated. In this paper, we explore how to improve the edge reconstruction accuracy from various aspects such as improving the network structure, employing hybrid loss functions and taking advantages of the non-local correlation of SPAD measurements. Meanwhile, we investigate the correlation between the edge reconstruction accuracy and the reconstruction accuracy of overall depth based on quantitative metrics. The experimental results show that the proposed method achieves superior performance in both edge reconstruction and overall depth reconstruction compared with other state-of-the-art methods. Besides, it proves that the improvement of edge reconstruction accuracy promotes the reconstruction accuracy of depth map.

Frequency upconversion imaging based on Hadamard coding.

A frequency upconversion imaging based on Hadamard coding is presented to remove the distorting effect on condition that the pump beam is tightly focused to optimize the conversion efficiency. The distortion caused by the convolution between the object field and the pump field is ascribed to the point spread function effect. In order to remove the blurring in an upconversion imaging system optimized by tight focused pump, the object is encoded by measurement matrices and the corresponding intensity of the converted field is measured. Thus the intensity distribution of the object can be calculated accurately by the measurements and the measurement matrix. The signal-to-noise ratio (SNR) is improved by employing the Hadamard matrix since the intensity of measured converted signal is far larger than the intensity of each pixel. The experimental results show the proposed method removes the distorting effect caused by the convolution. The converted image still has sharp edges on condition that the conversion efficiency is optimized by tight focusing the pump beam.

Generating non-uniformly correlated twisted sources.

The inverse method of proving the twistability of cross-spectral density (CSD) inevitably falls into spontaneous difficulties. Based on a nonnegative self-consistent design guideline for generating genuine CSDs introduced by Gori and Santarsiero, we demonstrate a feasible way for twisting partially coherent sources by sticking a Schell-model function to CSDs, which also determines the upper bound of the twisting strength. Analysis shows that the degree of coherence of a new class of twisted pseudo-Gaussian Schell-model beam is neither shift invariant nor shift-circular symmetric. In the presence of a vortex phase, the two different types of chiral phases affect each other and together control the propagation behavior. We further carry out an experiment to generate this non-uniformly correlated twisted beam using weighted superposition of mutually uncorrelated pseudo modes. The result is beneficial for devising nontrivial twisted beams and offers new opportunities.

Depth imaging denoising of photon-counting lidar.

Photon-counting lidar systems have difficulty reconstructing target depth images due to ambient noise. In this paper, we propose a novel way of using correlative photons and spatial correlations to reduce the false alarm probability. Experimental results show that the root mean square error of the depth image reconstructed by the proposed algorithm can be 1.68 times and 1.11 times better than that of the fast depth imaging denoising algorithm and log-matched filter estimation. The experimental results show that the proposed algorithm can effectively improve the reconstructed image of photon-counting lidar.

Fast optimization of coded apertures in X-ray computed tomography.

Coded aperture X-ray computed tomography (CAXCT) is a novel X-ray imaging system capable of reconstructing high quality images from a reduced set of measurements. Coded apertures are placed in front of the X-ray source in CAXCT so as to obtain patterned projections onto a detector array. Then, compressive sensing (CS) reconstruction algorithms are used to reconstruct the linear attenuation coefficients. The coded aperture is an important factor that influences the point spread function (PSF), which in turn determines the capability to sample the linear attenuation coefficients of the object. A coded aperture optimization approach was recently proposed based on the coherence of the system matrix; however, this algorithm is memory intensive and it is not able to optimize the coded apertures for large image sizes required in many applications. This paper introduces a significantly more efficient approach for coded aperture optimization that reduces the memory requirements and the execution time by orders of magnitude. The features are defined as the inner product of the vectors representing the geometric paths of the X-rays with the sparse basis representation of the object; therefore, the algorithm aims to find a subset of features that minimizes the information loss compared to the complete set of projections. This subset corresponds to the unblocking elements in the optimized coded apertures. The proposed approach solves the memory and runtime limitations of the previously proposed algorithm and provides a significant gain in the reconstruction image quality compared to that attained by random coded apertures in both simulated datasets and real datasets.

3D compressive spectral integral imaging.

A novel compressive 3D imaging spectrometer based on the coded aperture snapshot spectral imager (CASSI) is proposed. By inserting a microlens array (MLA) into the CASSI system, one can capture spectral data of 3D objects in a single snapshot without requiring 3D scanning. The 3D spatio-spectral sensing phenomena is modelled by computational integral imaging in tandem with compressive coded aperture spectral imaging. A set of focal stack images is reconstructed from a single compressive measurement, and presented as images focused on different depth planes where the objects are located. The proposed optical system is demonstrated with simulations and experimental results.

Adaptive compressed photon counting 3D imaging based on wavelet trees and depth map sparse representation.

We demonstrate a photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled along the wavelet trees and their depth map sparse representations. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that both the reflectivity and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17.5 seconds. In addition, we demonstrate that this technique has ability to image in presence of partially-transmissive occluders, and to directly acquire novelty images to find changes in a scene.

Colored adaptive compressed imaging with a single photodiode.

Computational ghost imaging is commonly used to reconstruct grayscale images. Currently, however, there is little research aimed at reconstructing color images. In this paper, we theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of the U, V components in the wavelet domain, the interdependence between luminance and chrominance, and human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required and offers better image quality compared to recovering the red (R), green (G), and blue (B) components separately in RGB color space. As the application of a single photodiode increases, our method shows great potential in many fields.

Approach for LIDAR signals with multiple returns.

Time-correlated single photon counting (TCSPC) and burst illumination laser (BIL) data can be used for depth reconstruction of a target surface; the problem is how to analyze the response for the reconstruction. We propose a fast-approach STMCMC (Simulated Tempering Markov Chain Monte Carlo) for LIDAR signals with multiple return, in order to obtain a complete characterization of a 3D surface by the laser range system. STMCMC is used to explore the spaces by the preset distributions instead of the prior distributions. Added active intervention tempering makes the Markov chain mix better through the temporary expansion of the solutions. The added step keeps the operation under control and yet retains the Markov characteristic of the operation. The theoretical analysis and the demonstrations on the practical data show flexible operation, and the parameters can be estimated to a high degree of accuracy.

Adaptive compressed sampling based on extended wavelet trees.

The theory of compressed sensing (CS) indicates that a signal that is sparse or compressible can be recovered from a relatively small number of nonadaptive linear measurements that is far below the Nyquist-Shannon limit. However, CS suffers from a huge stored and computational overhead when dealing with images of high resolution, taking tens of minutes or longer. In this work, we extend the concept of wavelet trees by adding the sibling relationship and propose an imaging strategy named adaptive compressed sampling based on extended wavelet trees (EWT-ACS). Exploiting both parent-children relationship and sibling relationship in extended wavelet trees, EWT-ACS predicts the locations of significant coefficients adaptively and samples the significant coefficients using a binary digital micromirror device directly. The simulation and experimental results reveal that the proposed strategy breaks through the limitation in CS, and the reconstruction time is reduced significantly. Due to its single-pixel detection mechanism, EWT-ACS shows great potential in many imaging applications.

[An information extraction method of spectral images based on 3D spectral angle statistics].

A novel information extraction method of spectral images based on 3D spectral angle statistics is proposed. By computing the spectral angle between adjacent pixels of the image in the horizontal, vertical and diagonal direction respectively, a 3D information statistical model was then constructed. This model reflects the similarity between adjacent pixels which represent some kind of materials. Uniform areas and edge information of the same material, which will be used for the training sample collection in supervised classification, can be extracted from the image if different threshold values are set and slices are extracted from different axes in the statistical model. Compared with the traditional statistical tools, such as the histogram and scatter diagram, this statistical method has higher robustness and reliability. And it can obtain more information extracted from the spectral images.