Journals >Optics and Precision Engineering
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2022
Volume: 30 Issue 10
11 Article(s)
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Information Sciences
Single-view 3D object reconstruction based on NFFD and graph convolution
Yuanfeng LIAN, Shoushuang PEI, and Wei HU
To address the issue of inaccurate single-view three-dimensional （3D） object reconstruction results caused by complex topological objects and the absence of irregular surface details， a novel single-view 3D object reconstruction method combining non-uniform rational B-spline free deformation with a graph convolution neTo address the issue of inaccurate single-view three-dimensional （3D） object reconstruction results caused by complex topological objects and the absence of irregular surface details， a novel single-view 3D object reconstruction method combining non-uniform rational B-spline free deformation with a graph convolution neural network is proposed. First， a control points generation network， which introduces the connection weight policy， is used for the feature learning of two-dimensional views to obtain their control points topology. Subsequently， the NURBS basis function is used to establish the deformation relationship between the vertex contours of the point cloud model. Finally， to enhance the details， a convolutional network embedded with a mixed attention module is used to adjust the position of the deformed point cloud to reconstruct complex topological structures and irregular surfaces efficiently. Experiments on ShapeNet data show that the average values of the CD and EMD indices are 3.79 and 3.94， respectively， and that good reconstruction is achieved on the Pix3D real scene dataset. In contrast to existing single view point cloud 3D reconstruction methods， the proposed method offers a higher reconstruction accuracy of 3D objects and demonstrates higher robustness..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1189 (2022)
Image registration based on residual mixed attention and multi-resolution constraints
Mingna ZHANG, Xiaoqi LÜ, and Yu GU
Medical image registration has great significance in clinical applications such as atlas creation and time-series image comparison. Currently， in contrast to traditional methods， deep learning-based registration achieves the requirements of clinical real-time； however， the accuracy of registration still needs to be impMedical image registration has great significance in clinical applications such as atlas creation and time-series image comparison. Currently， in contrast to traditional methods， deep learning-based registration achieves the requirements of clinical real-time； however， the accuracy of registration still needs to be improved. Based on this observation， this paper proposes a registration model named MAMReg-Net， which combines residual mixed attention and multi-resolution constraints to realize the non-rigid registration of brain magnetic resonance imaging （MRI）. By adding the residual mixed attention module， the model can obtain a large amount of local and non-local information simultaneously， and extract more effective internal structural features of the brain in the process of network training. Secondly， multi-resolution loss function is used to optimize the network to make the training more efficient and robust. The average dice score of the 12 anatomical structures in T1 brain MR images was 0.817， the average ASD score was 0.789， and the average registration time was 0.34 s. Experimental results demonstrate that the MAMReg-Net registration model can be better trained to learn the brain structure features to effectively improve the registration accuracy and meet clinical real-time requirements..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1203 (2022)
Single-image translation based on multi-scale dense feature fusion
Qihang LI, Long FENG, Qing YANG, Yu WANG, and Guohua GENG
To solve the problems of low image quality and poor detail features generated by the existing single image translation models， a single image translation model based on multi-scale dense feature fusion is proposed in this paper. First， in this model， the idea of multi-scale pyramid structure is used to downsample the oTo solve the problems of low image quality and poor detail features generated by the existing single image translation models， a single image translation model based on multi-scale dense feature fusion is proposed in this paper. First， in this model， the idea of multi-scale pyramid structure is used to downsample the original and target images to obtain input images of different sizes. Then， in the generator， images of different sizes are input into the dense feature module for style feature extraction， which are transferred from the original image to the target image， and the required translation image is generated through continuous game confrontation with the discriminator. Finally， dense feature modules are added in each stage of training by means of incremental growth generator training， which realizes the migration of generated images from global to local styles， and generates the required translation images. Extensive experiments have been conducted on various unsupervised images to perform image translation tasks. The experimental results demonstrate that in contrast to the existing methods， the training time of this method is shortened by 80%， and the SIFID value of the generated image is reduced by 22.18%. Therefore， the model proposed in this paper can better capture the distribution difference between the source and target domains， and improve the quality of image translation..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1217 (2022)
A multivariate information aggregation method for crowd density estimation and counting
Guanghui LIU, Qinmeng WANG, Xuanrun CHEN, and Yuebo MENG
In crowd density estimation， the crowd distribution and quantity in a crowded scene are counted， which is vital to safety systems and traffic control. A multivariate information aggregation method is proposed herein to solve difficult feature extractions， difficult spatial semantic information acquisitions， and insuffiIn crowd density estimation， the crowd distribution and quantity in a crowded scene are counted， which is vital to safety systems and traffic control. A multivariate information aggregation method is proposed herein to solve difficult feature extractions， difficult spatial semantic information acquisitions， and insufficient feature fusions in the crowd density estimation of high-density images. First， a multi-information extraction network is designed， where VGG-19 is used as a skeleton network to enhance the depth of feature extraction， and a multilayer semantic surveillance strategy is adopted to encode low-level features to improve the semantic representation of low-level features. Second， a multiscale contextual information aggregation network is designed based on spatial information embedded into the high-level feature space， and two lightweight spatial pyramiding structures with step-size convolution are applied to reduce the redundancy of model parameters during global multiscale context information aggregation. Finally， step convolution is performed at the end of the network to accelerate the network operation without affecting the precision. The ShanghaiTech， UCF-QNRF， and NWPU datasets are applied for a comparison experiment. The experimental results demonstrate that the MAE and MSE of Part_A of the ShanghaiTech dataset are 59.4 and 96.2， respectively， whereas those of Part_B are 7.7 and 11.9， respectively. The ultradense multiview-scene UCF-QNRF dataset indicates an MAE and MSE of 89.3 and 164.5， respectively. The high-density NWPU dataset indicates an MAE and MSE of 87.9 and 417.2， respectively. The proposed method performs better than the comparison method， as indicated by actual application results..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1228 (2022)
High frequency signal reconstruction based on compressive sensing and equivalent-time sampling
Ning JING, Dingyi YAO, Zhibin WANG, Minjuan ZHANG, and Rui ZHANG
A simple harmonic wave with frequency 10–100 GHz is collected by a domestic equivalence time optical sampling oscilloscope to measure and recover high-frequency signals in undersampling situations. There is a trigger sequence with a 5 ps delay resolution and 10 μs dynamic range in the oscilloscope. The trigger sequenceA simple harmonic wave with frequency 10–100 GHz is collected by a domestic equivalence time optical sampling oscilloscope to measure and recover high-frequency signals in undersampling situations. There is a trigger sequence with a 5 ps delay resolution and 10 μs dynamic range in the oscilloscope. The trigger sequence， generated by two steps of coarse and fine delayers， is used to drive the high band-wide sampler， and the sampling value is output by an ADC with a frequency of 50 kHz. In this advancement， the high-frequency signal is sampled with an increasing 5 ps delay every 20 μs. The compress ratio is approximately 106， and the sampling rate is far below the Nyquist law. With compressive sensing theory， the measurement matrix is constructed by Fourier translation and equivalence time sampling sequence and sparsify the signal measurement process. The measurement signal is reconstructed by solving an Ll-norm minimum problem. The results demonstrate that the signal with a frequency of 100 GHz can be undersampled and reconstructed with a mean square error below 5×10-5， implying that the dynastic range of the sampling oscilloscope should be expanded..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1240 (2022)
Image dehazing method based on adaptive bi-channel priors
Yutong JIANG, Zhonglin YANG, Mengqi ZHU, Yi ZHANG, and Lixia GUO
Image is an important source of information for modern warfare, and the quality of image decreases in foggy environment, which seriously hinders the ability of photoelectric reconnaissance and identification. In order to improve the effective utilization of images in foggy environment, an adaptive bi-channel prior imagImage is an important source of information for modern warfare, and the quality of image decreases in foggy environment, which seriously hinders the ability of photoelectric reconnaissance and identification. In order to improve the effective utilization of images in foggy environment, an adaptive bi-channel prior image dehazing method was developed. First, based on the dark channel prior and the bright channel prior theories, the hazy images are converted from RGB to HSV color space, and the thresholds of saturation and luminance components are used to detect white or light pixels and black or dark pixels in hazy images that do not satisfy the dark and light channel priors, respectively. Then, superpixels are selected as the local area for the calculation of the dark and bright channels, and the local transmittance and atmospheric light values are estimated. Finally, adaptive bi-channel priors are developed to rectify any incorrect estimation of transmission and atmospheric light values for both white and black pixels. The transmittance map and atmospheric light map are filtered by the guided filter, and then substituted into the atmospheric scattering model to obtain a clear dehaze image. Experimental results show that the dehazed image restores the true color, the visual effect is natural and clear, and the dehazing process of the image is accurately and efficiently achieved. The dehazing process is performed on the FRIDA database, the mean square error between the dehazed image and the ground truth using the method in this paper is better than that of the existing method, which are 15% lower than that yielded by the BiCP method..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1246 (2022)
Micro/Nano Technology and Fine Mechanics
Grating X-ray collimator supported by Si3N4 membrane with large aspect ratio written directly by electron beam
Yijie LI, Jun XIAO, Yifang CHEN, Xujie TONG, and Chengyang MU
The objective of this study was to develop a new X-ray collimator. Electron beam lithography （EBL） technology was coupled with electroplating and wet chemical etching technology to fabricate gold micron gratings involving a large area and high aspect ratio on a suspended silicon nitride membrane. The exposure dose in tThe objective of this study was to develop a new X-ray collimator. Electron beam lithography （EBL） technology was coupled with electroplating and wet chemical etching technology to fabricate gold micron gratings involving a large area and high aspect ratio on a suspended silicon nitride membrane. The exposure dose in the field splicing area was adjusted to solve the large area EBL problem. The grating line collapse in high-aspect ratio and high-density photoresist templates was overcome by using a reinforced structure. The thick photoresist spin-coated fracture on the 300-nm Si3N4 membrane was prevented by keeping an extremely thin layer of silicon （25 nm thick） under the thin Si3N4 membrane； therefore， improving the development process. The results demonstrated that the gold gratings with a 2-μm period， 5.5 aspect ratio， and 400-μm by 1000-μm area can modulate the 8-keV energy X-rays. The fabricated gold gratings can be used as detector collimators in line-parallel X-ray tomography systems or as source collimators in area-parallel X-ray tomography systems to improve the imaging speed..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1181 (2022)
Modern Applied Optics
Evaluation of light penetration of LED phototherapy apparatus in skin
Zefeng FENG, Peipei WANG, Xu YANG, Hu LIU, and Daxi XIONG
Low-level light therapy （LLLT） based on light-emitting diode （LED） light sources is a rapidly-developing and non-invasive light therapy technology. In contrast to a laser， LED phototherapy can easily produce a larger treatment spot area， while achieving almost the same treatment effect. When designing LED phototherapy Low-level light therapy （LLLT） based on light-emitting diode （LED） light sources is a rapidly-developing and non-invasive light therapy technology. In contrast to a laser， LED phototherapy can easily produce a larger treatment spot area， while achieving almost the same treatment effect. When designing LED phototherapy equipment and formulating phototherapy plans， too large an angle between the edge light and skin could affect the light penetration effect. Therefore， a skin model is required to analyze the light penetration for a large area of light spots. First， this study identified a general skin model sample based on the optical properties of skin tissue. Then， Monte Carlo simulation was used to trace the light emitted by the LED light source， and the wavelength distribution of light and different divergence angles were analyzed. Finally， the reliability of the sample model was verified based on the skin parameters of the human shoulder， forearm back， and buttocks. The simulation results demonstrated the transmission of a large-sized LED spot in the skin. Experimental simulations of different skin parts proved that the spot divergence half-angle needs to be controlled within 30°. This provides an important reference for the field of LED light therapy..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1139 (2022)
Application of cavity-enhanced gas Raman spectroscopy in gas logging
Andong KONG, Dewang YANG, Jinjia GUO, Lulu WU... and Yaqi WAN|Show fewer author(s)
Currently， gas logging relies primarily on the use of a gas chromatograph equipped with a flame ionization detector， whose sustaining flame must be distanced from the wellhead for safety. However， the elongated sampling tube delays the response time of detection. To meet the requirements of high sensitivity and rapid mCurrently， gas logging relies primarily on the use of a gas chromatograph equipped with a flame ionization detector， whose sustaining flame must be distanced from the wellhead for safety. However， the elongated sampling tube delays the response time of detection. To meet the requirements of high sensitivity and rapid multi-component gas detection in gas logging， a gas Raman spectroscopy detection system based on multi-reflection cavity enhancement is developed. This system is compact and portable and can detect numerous gases， including alkanes， hydrogen， and carbon dioxide， simultaneously with high sensitivity. In this study， we first describe the design and parameters of the gas Raman spectroscopy detection system， followed by testing the working performance of the Raman system for analyzing alkane gases and non-hydrocarbon gases. Experimental results demonstrate that the gas Raman spectroscopy detection system has good linearity for methane， hydrogen， and carbon dioxide detection. The limits of detection were 30， 201， and 495 ppm， respectively. Finally， the system was applied to the Shengli oilfield in Dongying， Shandong province， China. The experimental results of the Raman spectroscopy system are in good agreement with those from the gas chromatograph method. Unlike gas chromatographic devices， the developed Raman system has the capability of detecting hydrogen and offering advantages in time resolution. In conclusion， the Raman system design used in this study can fulfill the requirements of high sensitivity and rapid and multi-component detection in gas logging..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1151 (2022)
Simulation and verification of ultraviolet detection and location of abnormal discharge in power transmission line
Peng SONG, Xiaochen BAI, Xiaohuan LIN, Hua GUO, and Lijian ZHANG
Detecting abnormal discharge from transmission lines is crucial to the safe operation of power grids. To detect and locate the abnormal discharge of power equipment such as transmission lines and insulators， first， a corona discharge ultraviolet detection model is established based on the traversing tiny unit method； tDetecting abnormal discharge from transmission lines is crucial to the safe operation of power grids. To detect and locate the abnormal discharge of power equipment such as transmission lines and insulators， first， a corona discharge ultraviolet detection model is established based on the traversing tiny unit method； the ultraviolet transmission characteristics in the atmospheric channel are studied； and the relationship between the path loss and position of the detection equipment under different corona discharge directions is obtained. Second， by considering the relative position relationship between the detection device and discharge point， the corona discharge point location method is obtained based on the moving speed of the detection device and steering angle when receiving the maximum ultraviolet power. In addition， according to the detection distance and received ultraviolet power， a method for retrieving the simulated discharge power is obtained to determine whether there is an abnormal discharge. At last， an experimental platform is built to verify the algorithm outdoors. The experimental results demonstrate that， when the inspection distances are 15， 20， 25， and 30 m， respectively， the positioning error of the algorithm for the discharge point is <8%， and the error of retrieving the simulated discharge power is <10%， which proves the effectiveness of the algorithm..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1160 (2022)
3D mapping of submarine topography by laser line scanning based on pose correction by triangular displacement method
Chengcheng FAN, Xiaowei DE, Jinjia GUO, Youwen CAO... and Xilin ZHANG|Show fewer author(s)
As a three-dimensional （3D） imaging technology， laser line scanning has been widely used in underwater target detection and topographic mapping. In this study， a set of underwater laser line scanning experimental equipment is constructed， and images obtained using a calibrated laser line scanning device are processed vAs a three-dimensional （3D） imaging technology， laser line scanning has been widely used in underwater target detection and topographic mapping. In this study， a set of underwater laser line scanning experimental equipment is constructed， and images obtained using a calibrated laser line scanning device are processed via laser strip extraction， feature extraction， and matching； subsequently， 3D reconstruction is performed. To mitigate the interference of underwater scattering particles， the light strip extraction algorithm is used， where the single channel threshold and gray center methods are combined to improve the accuracy of light strip extraction. Consequently， a higher robustness is achieved compared with that afforded by other light strip center extraction algorithms. In terms of feature extraction and matching， an image-based triangular displacement estimation method for system positioning and pose correction is proposed herein. This method applies the principle of line-structured light triangulation to estimate the scene depth， matches feature points to estimate system displacement， and fuses point cloud data to complete pose correction. We performed a standard ball accuracy test in a laboratory using a self-developed line scanning system， and the error can be controlled below 1 mm within a distance of 800–2500 mm. In 2019 and 2020， we performed 3D imaging tests of deep-sea seabed terrains with an ROV using the abovementioned system. The tests successfully validated the pose correction method of the proposed triangular displacement method in a sloped seabed sand area， and further tests were performed on the seabed terrain under different conditions. The results demonstrate that the corrected laser line scanning imaging device can perform imaging rapidly and accurately with a high degree of morphology reduction..
Optics and Precision Engineering
- Publication Date: May. 25, 2022
- Vol. 30, Issue 10, 1170 (2022)
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