Enhancing the spatial resolution of time-of-flight based non-line-of-sight imaging via instrument response function deconvolution.

Non-line-of-sight (NLOS) imaging retrieves the hidden scenes by utilizing the signals indirectly reflected by the relay wall. Benefiting from the picosecond-level timing accuracy, time-correlated single photon counting (TCSPC) based NLOS imaging can achieve theoretical spatial resolutions up to millimeter level. However, in practical applications, the total temporal resolution (also known as total time jitter, TTJ) of most current TCSPC systems exceeds hundreds of picoseconds due to the combined effects of multiple electronic devices, which restricts the underlying spatial resolution of NLOS imaging. In this paper, an instrument response function deconvolution (IRF-DC) method is proposed to overcome the constraints of a TCSPC system's TTJ on the spatial resolution of NLOS imaging. Specifically, we model the transient measurements as Poisson convolution process with the normalized IRF as convolution kernel, and solve the inverse problem with iterative deconvolution algorithm, which significantly improves the spatial resolution of NLOS imaging after reconstruction. Numerical simulations show that the IRF-DC facilitates light-cone transform and frequency-wavenumber migration solver to achieve successful reconstruction even when the system's TTJ reaches 1200 ps, which is equivalent to what was previously possible when TTJ was about 200 ps. In addition, the IRF-DC produces satisfactory reconstruction outcomes when the signal-to-noise ratio (SNR) is low. Furthermore, the effectiveness of the proposed method has also been experimentally verified. The proposed IRF-DC method is highly applicable and efficient, which may promote the development of high-resolution NLOS imaging.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Underwater single photon 3D imaging with millimeter depth accuracy and reduced blind range.

Mono-static system benefits from its more flexible field of view and simplified structure, however, the backreflection photons from mono-static system lead to count loss for target detection. Counting loss engender range-blind, impeding the accurate acquisition of target depth. In this paper, count loss is reduced by introducing a polarization-based underwater mono-static single-photon imaging method, and hence reduced blind range. The proposed method exploits the polarization characteristic of light to effectively reduce the count loss of the target, thus improving the target detection efficiency. Experiments demonstrate that the target profile can be visually identified under our method, while the unpolarization system can not. Moreover, the ranging precision of system reaches millimeter-level. Finally, the target profile is reconstructed using non-local pixel correlations algorithm.

Ranging analysis of a moving target based on the dynamic instrument response function.

A ranging high-speed moving target with a high accuracy is challenging for a single-photon ranging system (SPRS). In this Letter, the dynamic instrument response function (IRF) is proposed to establish a dynamic discrete model (DDM) by introducing a velocity and a system timing resolution, which leads to better accuracy of cross-correlation results. And with the data of a dynamic Monte Carlo (DMC), the ranging accuracy can be improved with DDM.

Fast non-line-of-sight imaging based on product-convolution expansions.

Non-line-of-sight (NLoS) imaging reveals a hidden scene using indirect diffuse reflections. A common choice for analyzing the time-of-flight (ToF) data from a non-confocal system is an ellipsoid model whose operator is high-dimensional, leading to a computationally arduous task. In this Letter, the product-convolution expansions method is utilized to formulate the operator and its adjoint based on the observation of a shift-variant point spread function (PSF) in the ToF data. The operator and its adjoint are locally approximated as a convolution, which allows the forward and backward procedure to be computed efficiently through fast Fourier transform (FFT). Moreover, the low-rank approximation of the operator is obtained by matrix decompositions, further improving the computational efficiency. The proposed method is validated using publicly accessible datasets.

A Method for Improving the Detection Accuracy of the Spot Position of the Four-Quadrant Detector in a Free Space Optical Communication System.

In a free space optical communication system, the beacon light will lose most of its energy after long-distance transmission, and the background light from the universe will strongly interfere with it. The four-quadrant detector (4QD) has been widely used in optical communication systems as a high-precision spot position detection sensor. However, if the light signal falling on the 4QD is too weak, the electrical signal of the output position will be very weak, and it will easily be affected by or even submerged in noise. To solve this problem, we propose a method for improving the spot position detection accuracy. First, we analyzed the solution relationship between the actual position of the spot and the output signal of the 4QD, with a Gaussian spot as the incident light model. The output current signal of the detector was then transimpedance-amplified by an analog circuit and the output voltage signal with noise was digitally filtered. An error compensation factor and the gap size of the detector were introduced into the traditional spot position detection model. High-precision spot position information for the 4QD in a complex environment was then obtained using the improved spot position detection model. Experimental results show that the maximum spot position detection error for this method was only 0.0277 mm, and the root mean square error was 0.0065 mm, when the 4QD was in a high background noise environment. The spot position detection accuracy was significantly improved compared with traditional detection algorithms. Real-time detection can therefore be achieved in practical applications.

Lensless Computational Imaging Technology Using Deep Convolutional Network.

Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction.

Content-illumination coupling guided low-light image enhancement network.

Current low-light enhancement algorithms fail to suppress noise when enhancing brightness, and may introduces structural distortion and color distortion caused by halos or artifacts. This paper proposes a content-illumination coupling guided low-light image enhancement network (CICGNet), it develops a truss topology based on Retinex as backbone to decompose low-light image component in an end-to-end way. The preservation of content features and the enhancement of illumination features are carried out along with depth and width direction of the truss topology. Each submodule uses the same resolution input and output to avoid the introduction of noise. Illumination component prevents misestimation of global and local illumination by using pre- and post-activation features at different depth levels, this way could avoid possible halos and artifacts. The network progressively enhances the illumination component and maintains the content component stage-by-stage. The proposed algorithm demonstrates better performance compared with advanced attention-based low-light enhancement algorithms and state-of-the-art image restoration algorithms. We also perform extensive ablation studies and demonstrate the impact of low-light enhancement algorithm on the downstream task of computer vision. Code is available at: https://github.com/Ruini94/CICGNet .

Research Progress in the Synthesis of Targeting Organelle Carbon Dots and Their Applications in Cancer Diagnosis and Treatment.

With increasing knowledge about diseases at the histological, cytological to sub-organelle level, targeting organelle therapy has gradually been envisioned as an approach to overcome the shortcomings of poor specificity and multiple toxic side effects on tissues and cell-level treatments using the currently available therapy. Organelle carbon dots (CDs) are a class of functionalized CDs that can target organelles. CDs can be prepared by a "synchronous in situ synthesis method" and "asynchronous modification method." The superior optical properties and good biocompatibility of CDs can be preserved, and they can be used as targeting particles to carry drugs into cells while reducing leakage during transport. Given the excellent organelle fluorescence imaging properties, targeting organelle CDs can be used to monitor the physiological metabolism of organelles and progression of human diseases, which will provide advanced understanding and accurate diagnosis and targeted treatment of cancers. This study reviews the methods used for preparation of targeting organelle CDs, mechanisms of accurate diagnosis and targeted treatment of cancer, as well as their application in the area of cancer diagnosis and treatment research. Finally, the current difficulties and prospects for targeting organelle CDs are prospected.

Efficient learning-based blur removal method based on sparse optimization for image restoration.

In imaging systems, image blurs are a major source of degradation. This paper proposes a parameter estimation technique for linear motion blur, defocus blur, and atmospheric turbulence blur, and a nonlinear deconvolution algorithm based on sparse representation. Most blur removal techniques use image priors to estimate the point spread function (PSF); however, many common forms of image priors are unable to exploit local image information fully. In this paper, the proposed method does not require models of image priors. Further, it is capable of estimating the PSF accurately from a single input image. First, a blur feature in the image gradient domain is introduced, which has a positive correlation with the degree of blur. Next, the parameters for each blur type are estimated by a learning-based method using a general regression neural network. Finally, image restoration is performed using a half-quadratic optimization algorithm. Evaluation tests confirmed that the proposed method outperforms other similar methods and is suitable for dealing with motion blur in real-life applications.

Learning Non-Local Spatial Correlations To Restore Sparse 3D Single-Photon Data.

This paper presents a new algorithm for the learning of spatial correlation and non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime (fewer or less than one photon per pixel) or with a reduced number of scanned spatial points (pixels). The algorithm alternates between three steps: (i) extract multi-scale information, (ii) build a robust graph of non-local spatial correlations between pixels, and (iii) the restoration of depth and reflectivity images. A non-uniform sampling approach, which assigns larger patches to homogeneous regions and smaller ones to heterogeneous regions, is adopted to reduce the computational cost associated with the graph. The restoration of the 3D images is achieved by minimizing a cost function accounting for the multi-scale information and the non-local spatial correlation between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Various results based on simulated and real Lidar data show the benefits of the proposed algorithm that improves the quality of the estimated depth and reflectivity images, especially in the photon-starved regime or when containing a reduced number of spatial points.