Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor.

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time-photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.

A Simulation Method for Underwater SPAD Depth Imaging Datasets.

In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the advent of new single-photon imaging devices, single-photon avalanche diode (SPAD) devices, with high sensitivity and a high depth resolution, have become cutting-edge research tools in the field of underwater imaging. However, the high production costs and small array areas of SPAD devices make it very difficult to conduct underwater SPAD imaging experiments. To address this issue, we propose a fast and effective underwater SPAD data simulation method and develop a denoising network for the removal of backward-scattering interference in underwater SPAD images based on deep learning and simulated data. The experimental results show that the distribution difference between the simulated and real underwater SPAD data is very small. Moreover, the algorithm based on deep learning and simulated data for the removal of backward-scattering interference in underwater SPAD images demonstrates effectiveness in terms of both metrics and human observation. The model yields improvements in metrics such as the PSNR, SSIM, and entropy of 5.59 dB, 9.03%, and 0.84, respectively, demonstrating its superior performance.

Real-time binocular visual localization system based on the improved BGNet stereo matching framework.

Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high-precision networks is typically poor. Therefore, this study constructed a deep-learning-based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks. Experiments showed that the ability to detect the edges of foreground objects was enhanced. The network was used to build a system on the Xavier NX. The measurement accuracy and stability were better than those of traditional algorithms.

A Binocular Stereo-Imaging-Perception System with a Wide Field-of-View and Infrared- and Visible Light-Dual-Band Fusion.

With the continuous evolution of autonomous driving and unmanned driving systems, traditional limitations such as a limited field-of-view, poor ranging accuracy, and real-time display are becoming inadequate to satisfy the requirements of binocular stereo-perception systems. Firstly, we designed a binocular stereo-imaging-perception system with a wide-field-of-view and infrared- and visible light-dual-band fusion. Secondly we proposed a binocular stereo-perception optical imaging system with a wide field-of-view of 120.3°, which solves the small field-of-view of current binocular stereo-perception systems. Thirdly, For image aberration caused by the wide-field-of-view system design, we propose an ellipsoidal-image-aberration algorithm with a low consumption of memory resources and no loss of field-of-view. This algorithm simultaneously solves visible light and infrared images with an aberration rate of 45% and 47%, respectively. Fourthly, a multi-scale infrared- and visible light-image-fusion algorithm is used, which improves the situational-awareness capabilities of a binocular stereo-sensing system in a scene and enhances image details to improve ranging accuracy. Furthermore, this paper is based on the Taylor model-calibration binocular stereo-sensing system of internal and external parameters for limit correction; the implemented algorithms are integrated into an NVIDIA Jetson TX2 + FPGA hardware framework, enabling near-distance ranging experiments. The fusion-ranging accuracy within 20 m achieved an error of 0.02 m, outperforming both visible light- and infrared-ranging methods. It generates the fusion-ranging-image output with a minimal delay of only 22.31 ms at a frame rate of 50 Hz.

Calibration method for thermal infrared division-of-focal-plane polarimeters considering polarizer reflection characteristics.

Owing to manufacturing defects of micropolarizer arrays and differences in the pixel response of detectors, division-of-focal-plane (DoFP) polarimeters have severe nonuniformity, which affects the measurement accuracy of the polarimeters and the calculation of the polarization information. This study proposes a calibration method for thermal infrared DoFP polarimeters considering polarizer reflection characteristics. The temperature-controlled adjustable infrared polarized radiation source is calibrated by a division-of-time polarimeter and is, in turn, used to calibrate a thermal infrared DoFP polarimeter. Through laboratory blackbody and external scenes, the performance of the proposed method is compared to that of state-of-the-art techniques. The experimental results indicate that the proposed method effectively avoids overcalibration and improves the accuracy of polarization information.

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast.

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm's superiority in real-time processing, resource utilization, preservation of the image's details, and visual effects.

Self-calibration algorithm for installation angle deviation of bionic polarization compound eyes.

A self-calibration algorithm based on unsupervised optimization for polarizer installation angle deviation is proposed and used in a multi-aperture bionic polarization compound eye system. To simplify calibration operation, under the condition that the calibration-polarized light information is unknown, this algorithm fully exploits redundancy and random polarization information in the scene, and uses a non-convex multi-objective discrete parameter sorting optimization method to achieve angle self-calibration. Compared with ordinary calibration procedures, the algorithm requires less stringent conditions, achieves online calibration and is more accurate. It also can be applied to camera polarization arrays, division-of-focal-plane polarization cameras, and other polarization devices.

Automatic Suppression Method for Water Surface Glints Using a Division of Focal Plane Visible Polarimeter.

To address the problem of water surface detection imaging equipment being susceptible to water surface glints, this study demonstrates a method called De-Glints for suppressing glints and obtaining clear underwater images using a division of focal plane (DoFP) polarimeter. Based on the principle of polarization imaging, the best polarization angle and the image corresponding to the minimal average gray level of each pixel are calculated. To evaluate the improvement in image quality, the index E was designed. The results of indoor and outdoor experiments show that the error of the angle calculation of this method is within 10%, and the minimum error is only 3%. The E index is positively improved and can be relatively improved by 8.00 under the interference of strong outdoor glints, and the method proposed in this paper shows a good adaptive ability to the dynamic scene.

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method.

Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion. In this paper, an asymmetrically reweighted penalized least squares method based on spectral estimation (SEALS) is proposed. SEALS considers not only the uniform distribution of additive noise along the baseline but also the energy distribution of the signal above and below the fitted baseline. The energy distribution is estimated using inverse Fourier and autoregressive models to create a spectral estimation kernel. This kernel effectively optimizes and balances the asymmetric weight assigned to each data point. By doing so, it resolves the issue of local oversmoothing that is typically encountered in the asymmetrically reweighted penalized least squares method. This oversmoothing problem can negatively impact the iteration depth and accuracy of baseline fitting. In comparative experiments on simulated spectra, SEALS demonstrated a better baseline fitting performance compared to several other advanced baseline correction methods, both under moderate and strong fluorescence backgrounds. It has also been proven to be highly resistant to noise interference. When applied to real Raman spectra, the algorithm correctly restored the weak peaks and removed the fluorescence peaks, demonstrating the effectiveness of this method. The computation time of the proposed method was approximately 0.05 s, which satisfies the real-time baseline correction requirements of practical spectroscopy acquisition.

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5.

In order to meet the fast and accurate automatic detection requirements of equipment maintenance in railway tunnels in the era of high-speed railways, as well as adapting to the high dynamic, low-illumination imaging environment formed by strong light at the tunnel exit, we propose an automatic inspection solution based on panoramic imaging and object recognition with deep learning. We installed a hyperboloid catadioptric panoramic imaging system on an inspection vehicle to obtain a large field of view as well as to shield the high dynamic phenomena at the tunnel exit, and proposed a YOLOv5-CCFE object detection model based on railway equipment recognition. The experimental results show that the mAP@0.5 value of the YOLOv5-CCFE model reaches 98.6%, and mAP@0.5:0.95 reaches 68.9%. The FPS value is 158, which can meet the automatic inspection requirements of railway tunnel equipment along the line and has high practical application value.

InAs nanowire arrays for room-temperature ultra-broadband infrared photodetection.

Detection of short-wave infrared (SWIR) and mid-wave infrared (MWIR) emissions remains challenging despite their importance in many emerging applications, including night vision, space imaging and remote sensing. III-V compound semiconductor materials such as InAs have an ideal band gap covering a spectral regime from near-infrared (NIR), SWIR to MWIR. However, due to their high dark current, InAs photodetectors normally require a low-temperature operation, which has greatly limited their practical applications. Here, we report the engineering of InAs nanowire arrays to achieve efficient photodetection of light at wavelengths ranging from NIR to MWIR (3500 nm). By using selective area metal-organic vapour-phase epitaxy, we optimise the nanowire growth temperature and V/III ratio to achieve wurtzite (WZ)-based InAs nanowire arrays with a high WZ density of ∼67%. Due to the n-type background doping of the InAs nanowires and the p-type InAs substrate used for nanowire growth, a p-n junction is formed, and an ultrawide room-temperature photoresponse ranging from 500 to 3500 nm is obtained under zero bias. It is found that the waveguide modes supported by the InAs nanowires result in a high peak responsivity of 0.44 A W-1 and a detectivity of 1.25 × 1010 cm √Hz W-1 at a wavelength of 1600 nm, a bias voltage of only -0.1 V and a relatively high operating temperature of 150 K. Such a strong light trapping effect in the InAs nanowires also leads to significantly lower reflection compared to that observed in planar photodetectors, and thus strong absorption in the substrate extending the photoresponse up to the InAs bandgap edge of 3500 nm. Our work shows that through careful material optimisation and device design, InAs nanowire arrays are promising for the development of high-performance ultra-broadband infrared photodetectors for wavelengths ranging from NIR, SWIR to MWIR.

Small Zoom Mismatch Adjustment Method for Dual-Band Fusion Imaging System Based on Edge-Gradient Normalized Mutual Information.

Currently, automatic optical zoom setups are being extensively explored for their applications in search, detection, recognition, and tracking. In visible and infrared fusion imaging systems with continuous zoom, dual-channel multi-sensor field-of-view matching control in the process of synchronous continuous zoom can be achieved by pre-calibration. However, mechanical and transmission errors of the zoom mechanism produce a small mismatch in the field of view after co-zooming, degrading the sharpness of the fusion image. Therefore, a dynamic small-mismatch detection method is necessary. This paper presents the use of edge-gradient normalized mutual information as an evaluation function of multi-sensor field-of-view matching similarity to guide the small zoom of the visible lens after continuous co-zoom and ultimately reduce the field-of-view mismatch. In addition, we demonstrate the use of the improved hill-climbing search algorithm for autozoom to obtain the maximum value of the evaluation function. Consequently, the results validate the correctness and effectiveness of the proposed method under small changes in the field of view. Therefore, this study is expected to contribute to the improvement of visible and infrared fusion imaging systems with continuous zoom, thereby enhancing the overall working of helicopter electro-optical pods, and early warning equipment.

Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features.

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.

Magnetometry based on the effect of laser-induced plasmas in a sodium-containing environment.

The magneto-optical resonance response of sodium atoms generated by a high-energy solid-state pulse Nd:YAG laser is studied in different external magnetic fields. We investigate the resonance fluorescence signal of sodium atoms in a simulated sea fog environment based on the laser-induced plasma (LIP) effect. By ionizing an NaCl solution spray to generate sodium atoms in an atmospheric environment, we build a Bell-Bloom magneto-optical resonance system under laboratory conditions. With the help of laser-induced breakdown spectroscopy (LIBS) and extinction spectrum, we obtain sodium atoms with a lifetime of 250 µs. A narrowband tunable continuous wave (CW) 589-nm laser tuned at the D2 line with a modulation frequency around the Larmor frequency is used as the pump beam to polarize sodium atoms in the test magnetic field. We find that the magneto-optical resonance signals vary with different external magnetic fields and the positions of the resonance signal are consistent with the theoretical values. An intrinsic magnetometric sensitivity of 620.4 pT in a 1-Hz bandwidth is achieved.

Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra.

Unsupervised deep learning methods place increased emphasis on the process of cluster analysis of unknown samples without requiring sample labels. Clustering algorithms based on deep embedding networks have been recently developed and are widely used in data mining, speech processing and image recognition, but barely any of them have been used on spectra data. This study presents an unsupervised clustering algorithm for Raman spectra, called the convolutional variational autoencoder deep embedding clustering method (CVDE). It improves the network structure of the multi-layer perception (MLP) that is commonly used in other methods based on the VAE-GMM model, like VaDE, by replacing the hidden fully connected layer in the MLP with three convolution layers and two pooling layers for better clustering on the Raman spectra. The three convolution layers extend vertical channels to learn features, while pooling layers directly reduce the horizontal coding dimensions to prevent gradient explosion and overfitting. Furthermore, such network structures can easily incorporate the gradient-weighted class activation mapping (Grad-Cam) method to visualise the importance of spectral features for clustering, facilitating network tuning and spectral difference analysis. Moreover, through comparative experiments, CVDE has proven that it affords better clustering performance than current advanced clustering methods on not only the MNIST dataset but also two sets of Raman spectra: soybean oil Raman spectra with very small Raman feature differences and drug Raman spectra with a small data size. The clustering accuracies of these three datasets reach 94.48%, 90.43% and 98.70% respectively. Thus, CVDE is suitable for applications in static spectra, such as Raman spectra and LIBS spectra, and is more versatile than supervised methods in the spectral and chemical analysis fields.

Multi-Integration Time Adaptive Selection Method for Superframe High-Dynamic-Range Infrared Imaging Based on Grayscale Information.

With the development of superframe high-dynamic-range infrared imaging technology that extends the dynamic range of thermal imaging systems, a key issue that has arisen is how to choose different integration times to obtain an HDR fusion image that contains more information. This paper proposes a multi-integration time adaptive method, in order to address the lack of objective evaluation methods for the selection of superframe infrared images, consisting of the following steps: image evaluation indicators are used to obtain the best global exposure image (the optimal integration time); images are segmented by region-growing point to obtain the ambient/high-temperature regions, selecting the local optimum images with grayscale closest to the medium grayscale of the IR imaging system for the two respective regions (lowest and highest integration time); finally, the three images above are fused and enhanced to achieve HDR infrared imaging. By comparing this method with some existing integration time selection methods and applying the proposed method to some typical fusion methods, via subjective and objective evaluation, the proposed method is shown to have obvious advantages over existing algorithms, and it can optimally select the images from different integration time series images to form the best combination that contains full image information, expanding the dynamic range of the IR imaging system.

Residual Interpolation Integrated Pixel-by-Pixel Adaptive Iterative Process for Division of Focal Plane Polarimeters.

Residual interpolations are effective methods to reduce the instantaneous field-of-view error of division of focal plane (DoFP) polarimeters. However, their guide-image selection strategies are improper, and do not consider the DoFP polarimeters' spatial sampling modes. Thus, we propose a residual interpolation method with a new guide-image selection strategy based on the spatial layout of the pixeled polarizer array to improve the sampling rate of the guide image. The interpolation performance is also improved by the proposed pixel-by-pixel, adaptive iterative process and the weighted average fusion of the results of the minimized residual and minimized Laplacian energy guide filters. Visual and objective evaluations demonstrate the proposed method's superiority to the existing state-of-the-art methods. The proposed method proves that considering the spatial layout of the pixeled polarizer array on the physical level is vital to improving the performance of interpolation methods for DoFP polarimeters.

High-throughput quantitation of trace level melatonin in human milk by on-line enrichment liquid chromatography-tandem mass spectrometry.

The level of melatonin in human milk might be closely related to infant development and the building up of their circadian rhythms. The large population investigation on this topic would provide insights for the prevention and treatment of diseases related to early development and circadian rhythms. However, it has not been well studied. Trace level endogenous melatonin and difficulties in sample collection are among the challenges limiting the progress. High throughput analytical method with high specificity and sensitivity to determine the endogenous melatonin concentration is highly desired. A newly developed easily operated and high-throughput sensitive on-line enrichment liquid chromatography-tandem mass spectrometry (LC-MS/MS) method would be reported in this paper. Melatonin-d3 (MEL-d3) was used as a surrogate standard for the calibration curve and melatonin-d4 (MEL-d4) was used as an internal standard. Sample preparation was simply performed in 96-well plate by protein precipitation using acetonitrile (ACN). The supernatant was injected directly into the easily configured LC-MS/MS system with an enlarged sample loop and a mixer. Positive mode multiple reaction monitoring (MRM) was adopted for the measurement of melatonin in milk. 100 µL sample was used for analysis and the calibration curve linear range was 1-1000 pg mL-1. In three validation batches, the accuracy was within 11.0% deviation from the relative nominal concentration, whereas the intra- and inter-assay precision was ≤4.1% and ≤6.8% relative standard deviation (RSD), respectively. Although matrix effect was observed in the validation experiments, the stable isotope labeled internal standard (MEL-d4) could correct it and the overall relative matrix effect of MEL-d3/MEL-d4 was close to 100%. The overall spike recovery of the method was 101.7% with 5.1% RSD. Compared to currently reported methods, it could reach 1 pg mL-1 lower limit of quantification (LLOQ) with a smaller sample volume, sample preparation could be easily performed by automated liquid handling system and was more suitable for large population cohort studies on trace level endogenous melatonin determination.

Temporal and spatial error model for estimating the measurement precision of the division of focal plane polarimeters.

Temporal noise and spatial non-uniformity primarily limit the measurement precision of division of focal plane (DoFP) polarimeters, based on which this study proposes an error model for DoFP polarimeters. The closed-form expressions of the estimation error of the main polarization parameters (Stokes vector, degree of linear polarization, and angle of linear polarization) are derived. Compared with the existing error models for DoFP polarimeters in the presence of temporal noise, the proposed model modifies the normalization condition in traditional calibration methods of DoFP polarimeters and clarifies the selection rule of the coefficient matrix leading to more accurate precision estimation; and experiments using linearly polarized light on a real-world DoFP polarimeter prove its validity.

Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS.

Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.