Optical phased arrays, as an advanced beam control technology, have wide applications in fields such as optical communication, imaging systems, and radar systems. However, due to hardware errors and environmental interference, phase errors in optical phased array systems can lead to beam pointing deviation, increased spot divergence angle, and higher sidelobe energy, thereby affecting system performance. To address this issue, this study proposes a phase error compensation method based on Particle Swarm Optimization (PSO), optimizing the phase distribution of the array to improve beam focus and reduce sidelobe energy. To verify the performance of the algorithm, a one-dimensional 128-channel optical phased array simulation model is constructed, and beam optimization under different spot size constraints is simulated and compared with the gradient descent method. The experimental results show that the PSO algorithm effectively improves the main lobe energy concentration and suppresses sidelobe energy. Compared with traditional optimization methods, PSO demonstrates stronger adaptability in high-precision requirements and complex array scales.

The invisible defects on photovoltaic modules such as cracks, fragments, black spots, and shadings are important to the power efficiency, the lifespan, and the safety of the photovoltaic modules. In order to realize on-site imaging of such defects under high irradiance level, the optimization of the imaging system was proposed, including the band-pass near-infrared imaging system, the double-exposure background-light decrease, and no-linear image sharping. The image dynamic model of the electroluminescence or photoluminescence imaging under high irradiance level was established, and experimental device of the electroluminescence imaging for the silicon photovoltaic modules based on a band-pass InGaAs camera was set up. Experimental results show that the high quality images of the defects on the photovoltaic modules under the irradiance of 300W/m2 can be obtained by our method.

The collimating optical system represents a critical component of optical testing devices, with stringent technical specifications required in the field of laser beam quality measurement and calibration. A high-precision assembly and adjustment method for an off-axis hyperbolic optical system is proposed. The applicability of the compensation method and the aberration-free point method in verifying the conical coefficient k of hyperbolic mirrors is analyzed. Based on the parameters of the primary and secondary mirrors, these methods are selected for surface shape testing to achieve precise assembly of the mirrors. Considering the structural characteristics of the off-axis R-C system, mechanical end faces and a horizontal plane are utilized as reference points for alignment. This approach ensures parallelism between the optical axis of the interferometer, the normal of the flat mirror, and the optical axis of the primary mirror, establishing a proper relationship with the adjustment references. The correlation between the misalignment of the secondary mirror and the resulting wavefront aberration of the system is examined. Computer-aided adjustment facilitates six degrees of freedom in the alignment of the secondary mirror, enabling rapid convergence of the wavefront aberration. After adjustment, the system achieves a root mean square RMS wavefront aberration better than 0.0245@632.8nm, satisfying the specified design criteria.

Addressing the issue that traditional endoscopes rely solely on morphological observations, which making it difficult to accurately diagnose early or subtle symptoms, a dual-modal endoscope integrating high-definition imaging and Raman spectroscopy detection has been designed. Its aim is to achieve high-definition full-field imaging with visible light and precise focusing of Raman detection light in a small field of view. Adopting a common optical path design, the sharing of the front objective group and the relay system not only reduces the diameter of the endoscope, but also lowers the cost. In the imaging system, it achieves an F-number of 7.3, visible light high-definition imaging with a full field of view of 70° and a small aperture, ensures a modulation transfer function of no less than 0.12@175lp/mm at a working distance of 10mm, and maximum distortion controlled within 11.64%, these features effectively minimize aberrations and improving imaging quality. At the Raman detection end, a 785nm excitation wavelength is selected, with a spectral range covering 785~1100nm, and precise focusing is achieved. Compared to conventional endoscopes, it not only retains the morphological observation function but also acquires real-time information on molecular structure and material composition through Raman spectroscopy technology, thereby enhancing diagnostic accuracy.

To study the specific impact of optical elements on the performance of optical systems, Zernike polynomials are employed as analytical tools for performance evaluation. The optimization methods for optical elements and the theoretical foundation of Zernike polynomials are begun with, introducing the concepts of circular Zernike polynomials and annular Zernike polynomials. It also explores the relationship between circular Zernike polynomials and Seidel aberrations, as well as their application in wavefront fitting. A review of research achievements both domestically and internationally in the optimization of optical element surface shapes, structural optimization, and system imaging performance is presented, showcasing the role of Zernike polynomials in these areas. Furthermore, a analysis of the key factors influencing the optimization of optical elements is provided, highlighting how these factors affect the optimization results of Zernike polynomials. Finally, the development trends of Zernike polynomials in the optimization of optical elements are anticipated.

Digital holographic images are prone to distortion during the compression process, which degrades the quality of the reconstructed images and increases the difficulty of speckle noise removal. Moreover, existing speckle denoising methods require training different networks for varying degrees of compression distortion, limiting their flexibility in practical applications. In response to these issues, an unsupervised speckle noise removal method is proposed for digital holographic reconstructions towards compression distortion. Based on the quality factor of the compression process, a compression perception attention module is designed to dynamically adjust the network weights, achieving adaptive control over the denoising process. Secondly, a speckle noise correlation convolution is used in conjunction with a blind spot network architecture to prevent detail loss due to sampling density constraints. Finally, a nonlinear attention module is introduced to enhance the interaction of global information, allowing the network to more accurately capture speckle noise. Experimental results demonstrate that the proposed method effectively removes speckle noise while preserving more detailed information. Compared to existing methods, the proposed method achieves better results in both objective quality assessment and subjective visual perception.

In off-axis digital holograms, there exists a certain off-axis angle between the object light and the reference light, and the magnitude of the off-axis angle reflects the degree of separation of the spectral positions of the +1-order image and the -1-order image in the hologram spectrum, which solves the problem of the twin image mixing in the on-axis holography. However, the off-axis angle also introduces tilt aberration to the phase measurement, which affects the accuracy of the phase measurement. In order to eliminate the off-axis tilt distortion in digital holography, an adaptive off-axis tilt distortion compensation method based on Gaussian high-pass filter (GHPF) is proposed. By performing Fourier transform and high-pass frequency domain filtering on the phase image, and based on the characteristic that most of the energy is concentrated within a small circular region in the spectrum, the cutoff frequency of the filter is adaptively selected using an energy threshold. This allows for the extraction of high-frequency details and improvement in off-axis tilt distortion removal and phase imaging accuracy. The holograms captured by the holographic optical path based on the Mach-Zehnder interferometer are used for experimental verification, and the advantages of the method are analysed in comparison with other off-axis tilt aberration removal methods.

Terahertz waves demonstrate exceptional penetration capabilities in non-polar materials, with terahertz echo imaging generated by internal dielectric discontinuities offering high resolution and non-contact operation. These characteristics position terahertz technology as a research priority for field-based non-destructive testing of insulation components like high-voltage ceramic bushings. This study introduces theoretical models for terahertz wave transmission and reflection, presenting systematic methodologies for extracting material dielectric parameters using both transmission and reflection approaches. Through scanning imaging, the thickness distribution of the anti-pollution flashover coating on the external surface of the high-voltage ceramic sleeve specimen is detected, and the prefabricated gap defects inside the ceramic are also identified. This research demonstrates terahertz imaging's dual capability for surface coating evaluation and subsurface defect detection in high-voltage insulation components.

Point cloud segmentation is a critical step in tunnel construction for detecting overbreak and underbreak regions and measuring their volumes. However, due to the irregular contours of tunnels with overbreak and underbreak regions, achieving high segmentation accuracy is challenging, especially for objects near the contours that may cause interference. To address this issue, a Transformer-based model is proposed. The model employs a self-attention mechanism to capture long-range dependencies across the global domain and integrates a local information fusion module to combine local geometric and feature context, leveraging the inherent point distribution in 3D space. By adopting a DGCNN-like architecture, the model enhances its feature representation capabilities. Experiments were conducted on a constructed 3D point cloud dataset representing overbreak and underbreak tunnels, comparing the proposed model with DGCNN and Point Transformer. The results demonstrate that the proposed model outperforms the others in terms of inference speed, computational resource requirements, and segmentation accuracy, achieving an mIoU of 75.8% and showing significant performance improvements. This model not only provides technical support for the excavation of overbreak and underbreak regions but also enables point cloud segmentation for lined tunnels, facilitating visualized construction and enhancing engineering quality and efficiency.

Subjective speckle is widely used in various fields such as optical imaging and object detection, especially in the field of in-plane displacement measurement which has attracted much attention, and illumination laser is one of the key factors affecting its measurement. Firstly, based on the theory of information optics, the principle of lens imaging under spherical wave and plane wave illuminations is studied; Then an optical system of subjective speckle is constructed, and the in-plane displacements of scattered object are measured using digital image correlation method under spherical wave and plane wave illuminations, respectively. Finally, the influence of the two types of waves on the in-plane displacement measurement is analyzed and discussed based on the above principle. It is found that the two types of waves have little effect on the measurement error, but have a significant impact on the measurement limit, that is, the measurement limit of in-plane displacement under spherical wave illumination is much lower than that under plane wave illumination.

Circular targets are widely used in visual measurement fields such as augmented reality, photogrammetry, and structured light projection measurement systems. However, the eccentricity error of circular targets caused by perspective projection seriously affects the measurement accuracy. In order to improve the accuracy of projection center positioning, an accurate projection center positioning method with concentric circles as the target was proposed. This study mainly includes an improved projection center positioning method and iterative compensation considering lens distortion. The use of ratio invariance avoids the eccentricity error caused by perspective projection of circular targets, and then the improved projection center coordinates are added to the iterative calibration process to eliminate the influence of lens distortion on center positioning, so as to accurately locate the projection center. Experimental results show that the proposed method can accurately locate the projection center and has good performance in camera calibration.

Aiming at the problems of inaccurate measurement of human eye parameters and inconvenient operation brought by the semi-automation of refractometers, a fully automatic refractometer is designed by means of optics, mechanics, electronics and computing. An automatic positioning method for human eyes is proposed. The short-focus camera is used to realize the alignment between the eye and the camera plane, and the pupil camera is used to realize the focus of the longitudinal distance between the eye and the camera. The optimized template matching algorithm is used to locate the human eye area in the short-focus camera image. The pupil contour ellipse is fitted according to the pupil characteristics. Then, the offset between the center of the ellipse and the center of the camera in the plane X/Z direction is calculated, and the 3D moving platform is controlled to move to the center of the human eye. In distance focusing, the Hu invariant moment method is proposed to extract the middle line of the reflection target ring in the pupil camera image, and the ellipse is fitted to calculate the ellipse diameter. A method of calculating the longitudinal distance (Y direction) between the human eye and the camera by using the ratio of ellipse diameter-reflected light spot distance is proposed, and the 3D moving platform is controlled near or far from the human eye to achieve focusing location. The experimental results show that the accuracy rate of human eye positioning is as high as 98%, and no significant difference is found in positioning speed compared with the market prototype, making it suitable for practical application.

Existing fatigue driving detection models often suffer from limitations such as single evaluation criteria, insufficient adaptability, high computational demands, and performance degradation under low-light conditions at night. A fatigue state evaluation system based on adaptive threshold optimization is proposed. The system leverages linear polarization lighting technology to enhance image quality in low-light environments and integrates the YOLO-GM model with boundary constraint optimization to improve the selection and recognition of eye and mouth ROI under occlusion scenarios, thereby enhancing feature recognition accuracy. A multi-feature fusion-based fatigue state evaluation model is constructed, and an adaptive fatigue threshold determination method based on a binary decision tree is proposed to dynamically adjust evaluation thresholds, further improving classification accuracy. Experiments conducted on the YawDD and a self-constructed dataset demonstrate that the proposed model reduces the parameter size by 3.95MB compared to the original model, achieves a feature recognition accuracy of 95.08%, and reaches a fatigue state evaluation accuracy of 95%, with an average processing time of 88.5ms per frame. Given its ability to perform effectively under low-light conditions at night, combined with high real-time performance and strong adaptability to individual driver differences, the system is well-suited for integration into in-vehicle platforms with limited computational resources.

Compared to traditional noise reduction methods, deep learning-based denoising algorithms can denoise unseen types of noise, enhancing the visual quality of denoised images. However, deep learning networks require a large number of images for training, which is often not available in biomedical experiments where there is a lack of samples to train neural networks. In response to this situation, a zero-shot learning convolutional neural network is proposed, which generates different images through subsampling technology to be used alongside noisy images for the residual network to learn from. The loss function in the residual network combines residual loss, regularization loss, and guidance loss, while attention mechanisms are also incorporated into the convolutional layers. In the case of noise standard deviations of 10,25 and 50, the PSNR of the algorithm is 35.43dB, 29.93dB and 24.81dB. The experimental comparison with other noise reduction algorithms shows that this algorithm has better noise reduction effect in the case of low noise, and also shows comparable performance with other models in the case of high noise, which proves its high efficiency and stability in the case of limited samples.

Assessing the Signal-to-Noise Ratio Variability (SNR) of Landsat 8 Satellite OLI Images is crucial for ensuring the continuity of Landsat satellite data and expanding the application of long-term satellite data in various fields such as ecology, natural resources, and land use. This study is to calculate the SNR of Landsat 8 OLI images from 2014 to 2020, and to evaluate the change of SNR over the past seven years. The technique process is listed as follows: (1)The homogenous areas and the corresponding images are selected from the global areas in a variety of surface types including desert or Gobi, ocean, lakes, snow, and dense vegetation.(2)The mean and standard deviation of the homogenous areas are calculated using the Digital Number images. The SNR is calculated by the ratio between the mean and standard annually. (3) The change of the SNR from 2014 to 2020 is quantitatively analyzed, and the trend of SNR is evaluated. The results show that: The SNR of OLI images in each band remains at a certain level, meeting the requirements for global land surface change studies. Nevertheless, the SNR shows fluctuation change with a small amplitude over the past seven years. The SNR of OLI images in 2014 is the highest while that in 2020 is the lowest. The SNR of OLI images over the past seven years shows a potential downward trend. The results are of significance to the application on the analysis of global change using OLI images.

Hyperspectral image classification has gained widespread attention in recent years, particularly with the significant advances in the application of Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs handle pixel information in small, regular regions, GNNs excel at capturing features from irregular, superpixel regions. To combine the strengths of both, a novel hyperspectral classification network named the SpectraFuse GAT and Bipolar Self-Attention Fusion Network (SGBF) is proposed, which integrates Bipolar Self-Attention CNN and SpectraFuse GAT for high-quality hyperspectral classification. In the GNN component, the SpectraFuse GAT (SGAT) is introduced and the Spectral Refinement Module (SRM) is developed to enhance the spectral information extraction capability. In the CNN component, we incorporate the Bipolar Self-Attention mechanism (BSA) to capture spatial-spectral information. The experimental results demonstrate that SGBF performs exceptionally well across multiple datasets. On the Indian Pines dataset, SGBF achieved a classification accuracy of 91.59%, which is 13.2% higher than CNN and 12.23% higher than GNN methods. On the PaviaU dataset, the accuracy reached 98.54%, surpassing the current best method by 2.37%. These results validate the superiority and robustness of SGBF in hyperspectral image classification.