Fringe projection profilometry (FPP) suffers from heterogeneous phase unwrapping errors (PUEs) that are difficult to correct cooperatively. We propose a classification and adaptive correction method based on global structural priors. Building on spatial continuity and approximate low-rankness, the method classifies PUEs into three categories—structural gross, sparse gross, and random—and corrects them progressively through boundary preprocessing, structure-aware weighted low-rank optimization (SA-WLRO), and conservative median filtering. An optional adaptive backfilling step is then introduced, yielding a dual-mode approach: the core algorithm (without backfilling) and the backfilling mode (with backfilling enabled), which together enable a flexible trade-off between precision and data completeness. Systematic experiments on flat plate, composite geometric bodies, plaster sculptures, and stair model demonstrate that the proposed dual-mode approach achieves substantial improvements over four representative model-driven methods in all tested scenarios, which include planar surfaces, isolated discontinuities, complex curved surfaces, and steep height variations. Notably, on the stair model—the most challenging case— the core algorithm achieves an RMSE of 0.0437 rad at 97.55% VPP, while the backfilling mode raises the VPP to 99.99% with an RMSE of 0.3084 rad, still more accurate than the 0.4251 rad of the best competing method. Ablation studies confirm the necessity of each module. These results position the proposed dual-mode approach as an effective post-processing solution for high-precision FPP systems.

In the field of forensic document examination, accurately determining the chronological sequence of intersecting lines between seal ink and handwriting is a crucial technical step for verifying document authenticity, identifying contract tampering, and detecting forged signatures. This technique analyzes the physical superimposition relationship formed by the deposition of the two media on the paper substrate to provide objective scientific evidence for judicial practice. Although traditional methods such as microscopic imaging and mass spectrometry analysis have achieved some progress, they still suffer from common limitations including high equipment costs, complex operation, and potential damage to samples. This study proposes and validates an innovative non-destructive determination method that integrates structured light 3D reconstruction technology with deep learning algorithms. The research captures the microscopic 3D morphological features of the ink intersection area using a high-precision structured light scanning system and effectively eliminates noise interference caused by paper substrate undulation through Gaussian flattening technology. Subsequently, a multimodal fusion strategy combines 2D texture images with 3D depth information to construct a dataset rich in features. On this basis, a deep learning model based on an improved Residual Neural Network (ResNet) is designed, incorporating the ELU activation function and an EMA mechanism to enhance the model’s feature extraction capability and convergence stability. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 94.39% on the test set, fully validating its effectiveness and application potential in the non-destructive determination of ink stroke sequencing.

With the rapid advancement of aerospace technology, the maneuverability of spacecraft has increasingly improved, creating a pressing demand for star sensors with a high attitude update rate and high precision. Star identification, as the most complex and time-consuming algorithm of star sensors, faces stringent requirements for enhanced identification speed and an enhanced identification rate. Furthermore, as the space environment is becoming more complex, the need for star sensors with heightened detection sensitivity is growing to facilitate real-time and accurate alerts for various non-cooperative targets, which has led to a sharp increase in the number of high-magnitude navigation stars in the star catalog, significantly impeding the speed and rate of star identification. Traditional methods are no longer adequate to meet the current demand for star sensors with high identification speed and a high identification rate. Addressing these challenges, a voting-based star identification algorithm using a partitioned star catalog is proposed. Initially, a uniform partitioning method for the star catalog is introduced. Building on this, a navigation feature library using partitioned catalog neighborhoods as a basic unit is constructed. During star identification, a method based on a voting decision is employed for feature matching in the basic unit. Compared to conventional methods, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. The performance of the proposed algorithm is validated through a simulation experiment and nighttime star observation experiment. Experimental results indicate an average identification rate of 99.760% and an average identification time of 8.861 milliseconds, exhibiting high robustness against position errors, magnitude errors, and false stars. The proposed algorithm presents a clear advantage over other common star identification methods, meeting the current requirement for star sensors with high star identification speed and a high identification rate.

Phase-shifting interferometry is a noncontact optical measurement method with high sensitivity. This method is widely used in optical surface and deformation measurements. However, environmental vibrations can have a significant influence on the obtained measurement results, producing fringe jitter and interference pattern ambiguity. To address these issues and improve the stability of phase-shifting interferometry, the anti-vibration technique can be used. In this article, the anti-vibration technology is divided into active and passive categories. Active anti-vibration is used for vibration isolation, that is, weakening the intensity of the vibration signal transmitted to the interference system. Passive anti-vibration is used to eliminate the influence of vibration on interferometry. Several types of passive anti-vibration technologies have been developed so far. In this article, existing passive phase-shifting interference anti-vibration technologies are classified and compared with respect to the frame number and real-time performance. Furthermore, the development direction of phase-shifting interference measurement anti-vibration technology is discussed.

A wavefront detection method for large aperture optical system based on the sparse aperture sampling is proposed. The conversion matrix of Zernike coefficients between the sub-aperture and full aperture is derived. The full aperture wavefront is calculated by the wavefront of the subaperture and the conversion matrix. The condition number of the conversion matrix is applied to evaluate the accuracy of the full aperture wavefront. Numerical simulation is employed to verify the accuracy of wavefront detection. The simulation results indicate that the wavefront RMS of the full aperture decreases with increasing the fill factor, sub-aperture number and subaperture baseline length of the sparse aperture. The accuracy of the full aperture wavefront can also be improved by our detection method.

Objective The sparse aperture optical system employs multiple discrete sub-apertures to replace the full aperture and achieves the resolution equivalent to that of the full aperture optical system while reducing the volume, quality, and costs. The sub-aperture's wavefront aberrations of the sparse aperture optical system exert impacts on the imaging performance of the whole system. In most studies, the system's field of view is not taken into account during the analysis of the imaging performance and sub-apertures' wavefronts of the sparse aperture optical system. Starting from the generalized pupil function, this paper develops the sparse aperture imaging model considering the system's field of view, thereby providing a theoretical basis for predicting the imaging performance and image restoration of the sparse aperture optical system under different fields of view. Methods The generalized pupil function of the sparse aperture optical system considering the field of view is derived on the theoretical basis of double Zernike polynomials (DZPs). The modulation transfer function ( MTF) of the system is obtained by the Fourier transform. The Golay3 sparse aperture imaging system designed by the ZEMAX optical software is taken as an example. According to the design results, the coefficients of double Zernike polynomials are fitted. The theoretical calculation results and optical design results are compared to verify the sparse aperture imaging theory considering the field of view. The Wiener filter is constructed according to the optical transfer function (OTF) for image restoration to improve the imaging quality of the system under different fields of view. Results and Discussions According to the theoretical model, the results show that when the field of view is 0 degrees, the imaging of the sparse aperture optical system approaches the diffraction limit as shown in Fig. 3(a). Figs. 3(b)-(e) indicate that under the same field of view, the main lobe and side lobe of MTFs decrease rapidly, and the main lobe shows different divergent directions corresponding to the directions of the incident light. As the field of view rises, the main lobe of MTFs further narrows, and the imaging performance of the optical system decreases significantly. MTFs calculated by DZPs are similar to those obtained by ZEMAX software. The contrasts of each line pair in the image simulated by the sparse aperture optical system are calculated under different fields of view. The images are processed by the Wiener filter, and the contrast curves are drawn, as shown in Figs. 9 (a)-(d). The figures demonstrate that the image contrasts of each field of view in horizontal and vertical directions can be greatly improved by the Wiener filter. Under the same field of view and different directions, the restored image has different contrasts in the horizontal and vertical directions. As shown in Fig. 9(b), when the field of view is ( 0. 05 degrees, 0 degrees), the contrast ranges in the horizontal and vertical directions are 0. 84-0. 99 and 0. 62-0. 99, respectively. In Fig. 9(c), when the field of view is (0 degrees, 0. 05 degrees), the contrast ranges in the horizontal and vertical directions are 0. 44-0. 84 and 0. 890. 99, respectively. As the field of view further increases, the contrasts of the image processed by the Wiener filter gradually decrease. In Fig. 9(d), when the field of view is ( 0. 1 degrees, 0 degrees), the contrast range in the vertical direction of the image before and after restoration is 0. 13-0. 26 and 0. 30-0. 43, respectively. Conclusions The sub-aperture's wavefront of the sparse aperture optical system under a non-zero field of view is represented by the DZP. When the generalized pupil function is constructed, the MTFs of the system under different fields of view are calculated by the Fourier transform, and the optical design of the system is carried out by ZEMAX software. Upon the fitting of the DZPs, the calculated MTFs of the system are proven to be consistent with those of the ZEMAX software, which verifies the method of utilizing DZPs to describe the wavefront of the sparse aperture imaging system under different fields of view. The Wiener filter related to the field of view is constructed on the basis of the OTF of the optical system. The image restoration using the Wiener filter effectively improves the imaging quality of the sparse aperture optical system under different fields of view.

Point cloud registration is widely used in autonomous driving, SLAM, and 3D reconstruction, and it aims to align point clouds from different viewpoints or poses under the same coordinate system. However, point cloud registration is challenging in complex situations, such as a large initial pose difference, high noise, or incomplete overlap, which will cause point cloud registration failure or mismatching. To address the shortcomings of the existing registration algorithms, this paper designed a new coarse-to-fine registration two-stage point cloud registration network, CCRNet, which utilizes an end-to-end form to perform the registration task for point clouds. The multi-scale feature extraction module, coarse registration prediction module, and fine registration prediction module designed in this paper can robustly and accurately register two point clouds without iterations. CCRNet can link the feature information between two point clouds and solve the problems of high noise and incomplete overlap by using a soft correspondence matrix. In the standard dataset ModelNet40, in cases of large initial pose difference, high noise, and incomplete overlap, the accuracy of our method, compared with the second-best popular registration algorithm, was improved by 7.0%, 7.8%, and 22.7% on the MAE, respectively. Experiments showed that our CCRNet method has advantages in registration results in a variety of complex conditions.

In this study, a multifunctional high-vacuum system was established to measure the electro-optical conversion efficiency of metamaterial-based thermal emitters with built-in heaters. The system is composed of an environmental control module, an electro-optical conversion measurement module, and a system control module. The system can provide air, argon, high vacuum, and other conventional testing environments, combined with humidity control. The test chamber and sample holder are carefully designed to minimize heat transfer through thermal conduction and convection. The optical power measurements are realized using the combination of a water-cooled KBr flange, an integrating sphere, and thermopile detectors. This structure is very stable and can detect light emission at the μW level. The system can synchronously detect the heating voltage, heating current, optical power, sample temperatures (both top and bottom), ambient pressure, humidity, and other environmental parameters. The comprehensive parameter detection capability enables the system to monitor subtle sample changes and perform failure mechanism analysis with the aid of offline material analysis using scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction. Furthermore, the system can be used for fatigue and high-low temperature impact tests.

Gas sensing performance characterization systems are essential for the research and development of gas sensing materials and devices. Although existing systems are almost completely automatically operated, the accuracies of gas concentration control and of pressure control and the ability to simultaneously detect different sensor signals still require improvement. In this study, a high-precision gas sensing material characterization system is developed based on vacuum technology, with the objective of enabling the precise and simultaneous measurement of electrical responses. Because of the implementation of vacuum technology, the gas concentration control accuracy is improved more than 1600 times, whereas the pressure of the test ambient condition can be precisely adjusted between vacuum and 1.2 bar. The vacuum-assisted gas-exchanging mechanism also enables the sensor response time to be determined more accurately. The system is capable of performing sensitivity, selectivity, and stability tests and can control the ambient relative humidity in a precise manner. More importantly, the levels of performance of three different optical signal measurement set-ups were investigated and compared in terms of detection range, linearity, noise, and response time, based on which of their scopes of application were proposed. Finally, single-period and cyclical tests were performed to examine the ability of the system to detect optical and electrical responses simultaneously, both at a single wavelength and in a spectral region.

The point cloud data from actual measurements are often sparse and incomplete, making it difficult to apply them directly to visual processing and 3D reconstruction. The point cloud completion task can predict missing parts based on a sparse and incomplete point cloud model. However, the disordered and unstructured characteristics of point clouds make it difficult for neural networks to obtain detailed spatial structures and topological relationships, resulting in a challenging point cloud completion task. Existing point cloud completion methods can only predict the rough geometry of the point cloud, but cannot accurately predict the local details. To address the shortcomings of existing point cloud complementation methods, this paper describes a novel network for adaptive point cloud growth, MAPGNet, which generates a sparse skeletal point cloud using the skeletal features in the composite encoder, and then adaptively grows the local point cloud in the spherical neighborhood of each point using the growth features to complement the details of the point cloud in two steps. In this paper, the Offset Transformer module is added in the process of complementation to enhance the contextual connection between point clouds. As a result, MAPGNet improves the quality of the generated point clouds and recovers more local detail information. Comparing our algorithm with other state-of-the-art algorithms in different datasets, experimental results show that our algorithm has advantages in dense point cloud completion.

Invalid points, such as shadow and background, in the captured fringe patterns of fringe projection profilometry (FPP) are often inevitable due to the limited field of view measurements of three-dimensional (3D) imaging equipment. To ensure the quality of 3D reconstruction data, these invalid points must be identified and removed. FPP captures co-frequency-based fringe pattern sequences and approximately distributes this data along an ideal cosine curve. We propose an invalid points removal method based on an error energy function. By analyzing the relationship between the pixel values of a series of captured co-frequency patterns and an ideal cosine curve, we quantize the error energy by using a Gaussian weighted Euclidean distance. An improved Gaussian filtering method based on modulation intensity is used to significantly increase the error energy difference between the valid points of the target and the invalid points of the shadow/background area. Finally, the points whose error energy is greater than a certain error threshold are removed as invalid points. Experimental results indicate that this method can efficiently remove invalid points in fringe patterns and performs better than existing traditional methods.

In order to solve the problems of uneven illumination and low accuracy in the automatic recognition system of pointer instruments, an Otsu algorithm and an improved Hough algorithm are proposed to process the instrument image. Firstly, the instrument image is preprocessed to enhance the characteristics of the instrument pointer area, Otsu algorithm performs the image binarization to segment pixels that only belong to the pointer, and the improved Hough transform is performed to detect the pointer. By quickly extracting the center pixel point of the dial pointer's connected area, combined with the dial center constraint, the double threshold Hough is performed on the extracted pixel point transform straight line detection. Experimental results show that the proposed algorithm detects the meter reading error at about 4.76% which meets the system accuracy requirements.

针对工业上紫外光功率检测精度低、辐射大、功耗高等问题.提出一种基于低功耗单片机MSP430为核心控制器、两个紫外光电传感器为探测器、无线LoRa为数据传输方式的设计方案.该方案采用双传感器,通过减法放大电路实现光电转换和环境光滤除,并采用高精度可编程AD采集芯片实现对信号的可控放大以及将模拟量转换成数字量传输至主控制器MSP430,利用改进的滑动平均滤波法对采集的数据进行数字滤波处理,并计算出相应的功率值,通过无线Lora进行数据传输至上位机进行实时显示.研究与实验表明:整个系统运行稳定、精度高、功耗低、能远程实时检测紫外光功率变化,可广泛用于工业紫外光检测领域上.

Tracking objects over time, i.e., identity (ID) consistency, is important when dealing with multiple object tracking (MOT). Especially in complex scenes with occlusion and interaction of objects this is challenging. Significant improvements in single object tracking (SOT) methods have inspired the introduction of SOT to MOT to improve the robustness, that is, maintaining object identities as long as possible, as well as helping alleviate the limitations from imperfect detections. SOT methods are constantly generalized to capture appearance changes of the object, and designed to efficiently distinguish the object from the background. Hence, simply extending SOT to a MOT scenario, which consists of a complex scene with spatially mixed, occluded, and similar objects, will encounter problems in computational efficiency and drifted results. To address this issue, we propose a binary-channel verification model that deeply excavates the potential of SOT in refining the representation while maintaining the identities of the object. In particular, we construct an integrated model that jointly processes the previous information of existing objects and new incoming detections, by using a unified correlation filter through the whole process to maintain consistency. A delay processing strategy consisting of the three parts—attaching, re-initialization, and re-claiming—is proposed to tackle drifted results caused by occlusion. Avoiding the fuzzy appearance features of complex scenes in MOT, this strategy can improve the ability to distinguish specific objects from each other without contaminating the fragile training space of a single object tracker, which is the main cause of the drift results. We demonstrate the effectiveness of our proposed approach on the MOT17 challenge benchmarks. Our approach shows better overall ID consistency performance in comparison with previous works.

Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy.

Under the dynamic working conditions for a star sensor, motion blur will appear in a star because of its energy dispersion in the process of imaging, which leads to a decrease in the signal to noise ratio (SNR) and makes the blurred region difficult to extract. Meanwhile, this causes a degradation in star centroid positon accuracy and attitude accuracy in the star sensor. Therefore, a restoration method for blurred star images based on region filters is presented in this paper, which simultaneously concentrates on the improvement of SNR and star centroid accuracy. Firstly, the kinematic models of a star centroid under different conditions are set up based on the characteristics of star sensors. Secondly, the motion trail of star centroid is determined based on the kinematic model, allowing the star blurred region to be extracted. The images inside and outside the star blurred region are then preprocessed by image processing algorithm respectively. Finally, the blurred star image is restored by an image restoration algorithm. The experiment results indicate that under the dynamic condition of 2 degrees/s, the region filter algorithm can effectively improve the SNR of a blurred star image. In restored images, the error of star centroid is less than 0.1 pixels, which can satisfy the requirements for star sensor of high centroid accuracy.

Aiming at the problem that small constant current source power, inconvenient dimming, and the noise caused by its switching characteristics is large, an intelligent optimized constant current driving design method with small size, high power, and good ripple effect is proposed. The design is improved on the basis of the previous low current, and the switching characteristics of the output current are converted to linear through feedback regulation, supplemented by switching the RCD loop (peak absorption loop) and constructing an optimized high-current loop PCB layout. The light source detects the current-to-pulse-width precision dimming, and the output can perfectly suppress the switching noise. The design method can realize remote digital dimming of the upper computer network, internal and external analog dimming, and intelligent control of multiple on-board cascades. Experiments show that the constant current drive output current can reach 10 A, the power can reach 370 W, the output current error is less than 1%, the drive light source has stable optical power, uniform illumination distribution, good ripple effect, stable and reliable.

Background Phase-shifting interferometry is a kind of important technique used in optical interference metrology. This technique has high precision and good stability, which has been widely used in scientific research and industrial production. Methods This paper proposes a new method to estimate global phase shift from two interferograms. This method performs algebraic calculation of two interferograms with the assistance of Hilbert transform. An iterative approach is used for the attempted phase to ensure that the minimum of assessment function is obtained. Results The simulated result indicate that the maximum calculation error of the global phase-shifting is 1.5%. And then we use experimental data to verify the performance of this method. Conclusions The method proposed in this article is simple but precise, and can cope with interferograms with uneven background and modulation, non-periodic apodization, and random noises. It does not require any specific carrier frequency of the measured interferogram or any adjustment of range of integration in accordance with the carrier frequency.

A system for computer video interface automatic detection was proposed based on FPGA NIOS II soft core,from the application point of view,when computers out of the factory.Computer plays a special video,and transmits the image to the detection equipment by video interface.Detection equipment converts HDMI, DP video signal into VGA RGB signal format through the video conversion chip controlled by NIOS II soft core.Detection equipment detects line signal,field Signal, and then puts the values of pixel RGB in the SRAM.Computer reads data from detection equipment through the high-speed transmission interface.It is proved by experiments that the video interface de-tection method can save labor cost and has a high detection efficiency.The detection error rate is less than 1% and equipment can detect interface which outputs 4K video 30 frames per second.

An optical system misalignment solution-based method for evaluating the stability of an optical-mechanical structure. In the method, a system misalignment is solved according to a wave surface deviation of an optical system at an initial time point and a time point to be evaluated; a corresponding compensator is adjusted according to the system misalignment; a wave surface deviation of the adjusted system and the system at the initial time point is used as a criterion of stability evaluation, and the solved misalignment shows long-term instability of the optical-mechanical structure within the time period. The method is suitable for real-time evaluation of the long-term stability of the optical system with a complex optical-mechanical structure, and solves the problem that a conventional stability evaluation method is limited by spatial positions.