Pub Date : 2021-01-15DOI: 10.12086/OEE.2021.200118
Wei Boyan, Tian Qingguo, Ge Baozhen
Aiming at the low adaptability of blurring noise of target feature points in traditional calibration methods, a calibration method based on the color-coded phase-shifted fringe is proposed. Using a liquid crystal display panel as the calibration target, horizontal and vertical color-coded phase-shifted stripes are displayed in sequence; the orthogonal phase-shifted stripes are obtained by separating color channels; based on the phase-shifteg theory, the intersections of the orthogonal phase truncation lines are calculated as the feature points. After changing the target position multiple times and extracting feature points, the plane-based camera calibration technique is applied to realize the calibration of both the single camera and the binocular system. Furthermore, color-coded phase-shift circles are added to four corners of the target pattern to automatically extract and sort feature points. Accordingly, the efficiency of calibration is promoted. The experimental results indicate that when the target image is blurred, the reprojection error of the single-camera calibration is 0.15 pixels, and the standard deviation of the binocular system measurement after calibration is 0.1 mm.
{"title":"Camera calibration based on color-coded phase-shifted fringe","authors":"Wei Boyan, Tian Qingguo, Ge Baozhen","doi":"10.12086/OEE.2021.200118","DOIUrl":"https://doi.org/10.12086/OEE.2021.200118","url":null,"abstract":"Aiming at the low adaptability of blurring noise of target feature points in traditional calibration methods, a calibration method based on the color-coded phase-shifted fringe is proposed. Using a liquid crystal display panel as the calibration target, horizontal and vertical color-coded phase-shifted stripes are displayed in sequence; the orthogonal phase-shifted stripes are obtained by separating color channels; based on the phase-shifteg theory, the intersections of the orthogonal phase truncation lines are calculated as the feature points. After changing the target position multiple times and extracting feature points, the plane-based camera calibration technique is applied to realize the calibration of both the single camera and the binocular system. Furthermore, color-coded phase-shift circles are added to four corners of the target pattern to automatically extract and sort feature points. Accordingly, the efficiency of calibration is promoted. The experimental results indicate that when the target image is blurred, the reprojection error of the single-camera calibration is 0.15 pixels, and the standard deviation of the binocular system measurement after calibration is 0.1 mm.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80342539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-15DOI: 10.12086/OEE.2021.200077
Yanhui Ran, H. Chunjie, Li Wei
We propose and experimentally demonstrate a novel in-band optical signal-to-noise ratio (OSNR) monitoring technique that uses a commercially available widely tunable optical bandpass filter to sample the measured optical power as input features of Gaussian process regression (GPR) can accurately estimate the large dynamic range OSNR and is not affected by the configuration of the optical link, and has the characteristics of distributed and low cost. Experimental results for 32 Gbaud PDM-16QAM signals demonstrate OSNR monitoring with the root mean squared error (RMSE) of 0.429 dB and the mean absolute error (MAE) of 0.294 dB within a large OSNR range of -1 dB~30 dB. Moreover, our proposed technique is proved to be insensitive to chromatic dispersion, polarization mode dispersion, nonlinear effect, and cascaded filtering effect (CFE). Furthermore, our proposed technique has the potential to be employed for link monitoring at the intermediation nodes without knowing the transmission information and is more convenient to operate because no calibration is required.
{"title":"A novel optical signal-to-noise ratio monitoring technique based on Gaussian process regression","authors":"Yanhui Ran, H. Chunjie, Li Wei","doi":"10.12086/OEE.2021.200077","DOIUrl":"https://doi.org/10.12086/OEE.2021.200077","url":null,"abstract":"We propose and experimentally demonstrate a novel in-band optical signal-to-noise ratio (OSNR) monitoring technique that uses a commercially available widely tunable optical bandpass filter to sample the measured optical power as input features of Gaussian process regression (GPR) can accurately estimate the large dynamic range OSNR and is not affected by the configuration of the optical link, and has the characteristics of distributed and low cost. Experimental results for 32 Gbaud PDM-16QAM signals demonstrate OSNR monitoring with the root mean squared error (RMSE) of 0.429 dB and the mean absolute error (MAE) of 0.294 dB within a large OSNR range of -1 dB~30 dB. Moreover, our proposed technique is proved to be insensitive to chromatic dispersion, polarization mode dispersion, nonlinear effect, and cascaded filtering effect (CFE). Furthermore, our proposed technique has the potential to be employed for link monitoring at the intermediation nodes without knowing the transmission information and is more convenient to operate because no calibration is required.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87241359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.12086/OEE.2021.210173
K. Yu, Xingyue Zhu, Chi Wu
{"title":"Design and experiment of a tunable narrow-passband deep UV light source","authors":"K. Yu, Xingyue Zhu, Chi Wu","doi":"10.12086/OEE.2021.210173","DOIUrl":"https://doi.org/10.12086/OEE.2021.210173","url":null,"abstract":"","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84268059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
: Due to the small scale and weak energy of the infrared dim small target, the background must be suppressed to enhance the target in order to ensure the performance of detection and tracking of the target in the later stage. In order to improve the ability of gradient reciprocal filter to suppress the clutter texture and reduce the interference of the residual texture to the target in the difference image, an adaptive gradient reciprocal filtering algorithm (AGRF) is proposed in this paper. In the AGRF, the adaptive judgment threshold and the adaptive relevancy coefficient function of inter-pixel correlation in the local region are determined by analyzing the distribution characteristics and statistical numeral characteristic of the background region, clutter texture, and target. Then the element value of the adaptive gradient reciprocal filter is determined by combining the relevancy coefficient function and the gradient reciprocal function. Experimental results indicate that the sensitivity of the AGRF algorithm to the clutter texture is significantly lower than that of the traditional gradient reciprocal filtering algorithm under the premise of the same target enhancement performance. Compared with the other nine algorithms, the AGRF algorithm has better signal-to-noise ratio gain (SNRG) and background suppress factor (BSF).
{"title":"Background suppression for infrared dim small target scene based on adaptive gradient reciprocal filtering","authors":"Biao Li, Zhiyong Xu, Chen Wang, Jianlin Zhang, Xiangru Wang, Xiangsuo Fan","doi":"10.12086/OEE.2021.210122","DOIUrl":"https://doi.org/10.12086/OEE.2021.210122","url":null,"abstract":": Due to the small scale and weak energy of the infrared dim small target, the background must be suppressed to enhance the target in order to ensure the performance of detection and tracking of the target in the later stage. In order to improve the ability of gradient reciprocal filter to suppress the clutter texture and reduce the interference of the residual texture to the target in the difference image, an adaptive gradient reciprocal filtering algorithm (AGRF) is proposed in this paper. In the AGRF, the adaptive judgment threshold and the adaptive relevancy coefficient function of inter-pixel correlation in the local region are determined by analyzing the distribution characteristics and statistical numeral characteristic of the background region, clutter texture, and target. Then the element value of the adaptive gradient reciprocal filter is determined by combining the relevancy coefficient function and the gradient reciprocal function. Experimental results indicate that the sensitivity of the AGRF algorithm to the clutter texture is significantly lower than that of the traditional gradient reciprocal filtering algorithm under the premise of the same target enhancement performance. Compared with the other nine algorithms, the AGRF algorithm has better signal-to-noise ratio gain (SNRG) and background suppress factor (BSF).","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87892418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.12086/OEE.2021.210123
Junqing Zhang, Yiping Wu, Shenghao Chen, Shiyi Gu, L. Sun, Mingru Zhou, Lin Chen
{"title":"Optimized bow-tie metasurface and its application in trace detection of lead ion","authors":"Junqing Zhang, Yiping Wu, Shenghao Chen, Shiyi Gu, L. Sun, Mingru Zhou, Lin Chen","doi":"10.12086/OEE.2021.210123","DOIUrl":"https://doi.org/10.12086/OEE.2021.210123","url":null,"abstract":"","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81929787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.12086/OEE.2020.200036
Hanshen Chen, M. Yao, Qu Xin-yu
Crack detection is one of the most important works in the system of pavement management. Cracks do not have a certain shape and the appearance of cracks usually changes drastically in different lighting conditions, making it hard to be detected by the algorithm with imagery analytics. To address these issues, we propose an effective U-shaped fully convolutional neural network called UCrackNet. First, a dropout layer is added into the skip connection to achieve better generalization. Second, pooling indices is used to reduce the shift and distortion during the up-sampling process. Third, four atrous convolutions with different dilation rates are densely connected in the bridge block, so that the receptive field of the network could cover each pixel of the whole image. In addition, multi-level fusion is introduced in the output stage to achieve better performance. Evaluations on the two public CrackTree206 and AIMCrack datasets demonstrate that the proposed method achieves high accuracy results and good generalization ability.
{"title":"Pavement crack detection based on the U-shaped fully convolutional neural network","authors":"Hanshen Chen, M. Yao, Qu Xin-yu","doi":"10.12086/OEE.2020.200036","DOIUrl":"https://doi.org/10.12086/OEE.2020.200036","url":null,"abstract":"Crack detection is one of the most important works in the system of pavement management. Cracks do not have a certain shape and the appearance of cracks usually changes drastically in different lighting conditions, making it hard to be detected by the algorithm with imagery analytics. To address these issues, we propose an effective U-shaped fully convolutional neural network called UCrackNet. First, a dropout layer is added into the skip connection to achieve better generalization. Second, pooling indices is used to reduce the shift and distortion during the up-sampling process. Third, four atrous convolutions with different dilation rates are densely connected in the bridge block, so that the receptive field of the network could cover each pixel of the whole image. In addition, multi-level fusion is introduced in the output stage to achieve better performance. Evaluations on the two public CrackTree206 and AIMCrack datasets demonstrate that the proposed method achieves high accuracy results and good generalization ability.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87737407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.12086/OEE.2020.200006
Ruan Yong, Xun Tianrong, Yang Tao, Tang Tao
In the image-based tip-tilt mirror control system, the closed-loop performance and bandwidth of the system and are limited due to the influence of sensor sampling frequency and system delay. Under the condition of limited bandwidth, this paper proposes to use linear encoder to measure the position, and get the rate signal by differ-ence. The position-rate feedback control based on the image sensor system is realized to improve the error sup-pression ability of the tip-tilt mirror control system. Because of the addition of rate feedback, the control system has differential characteristics. When the rate feedback closed-loop is completed, the image position loop has integral characteristic. At this time, a PI controller is used to stabilize the system, which makes the system rise from zero type to two type system, and improves the error suppression ability of the system. Simulation and experiment show that this method can effectively improve the closed-loop performance of the tracking control system in low frequency domain.
{"title":"Position-rate control for the time delay control system of tip-tilt mirror","authors":"Ruan Yong, Xun Tianrong, Yang Tao, Tang Tao","doi":"10.12086/OEE.2020.200006","DOIUrl":"https://doi.org/10.12086/OEE.2020.200006","url":null,"abstract":"In the image-based tip-tilt mirror control system, the closed-loop performance and bandwidth of the system and are limited due to the influence of sensor sampling frequency and system delay. Under the condition of limited bandwidth, this paper proposes to use linear encoder to measure the position, and get the rate signal by differ-ence. The position-rate feedback control based on the image sensor system is realized to improve the error sup-pression ability of the tip-tilt mirror control system. Because of the addition of rate feedback, the control system has differential characteristics. When the rate feedback closed-loop is completed, the image position loop has integral characteristic. At this time, a PI controller is used to stabilize the system, which makes the system rise from zero type to two type system, and improves the error suppression ability of the system. Simulation and experiment show that this method can effectively improve the closed-loop performance of the tracking control system in low frequency domain.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80619363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.12086/OEE.2020.200007
Z. Yuanyuan, Shi Shengxian
As a new generation of the imaging device, light-field camera can simultaneously capture the spatial position and incident angle of light rays. However, the recorded light-field has a trade-off between spatial resolution and angular resolution. Especially the application range of light-field cameras is restricted by the limited spatial resolution of sub-aperture images. Therefore, a light-field super-resolution neural network that fuses multi-scale features to obtain super-resolved light-field is proposed in this paper. The deep-learning-based network framework contains three major modules: multi-scale feature extraction, global feature fusion, and up-sampling. Firstly, inherent structural features in the 4D light-field are learned through the multi-scale feature extraction module, and then the fusion module is exploited for feature fusion and enhancement. Finally, the up-sampling module is used to achieve light-field super-resolution. The experimental results on the synthetic light-field dataset and real-world light-field dataset showed that this method outperforms other state-of-the-art methods in both visual and numerical evaluations. In addition, the super-resolved light-field images were applied to depth estimation in this paper, the results illustrated that the disparity map was enhanced through the light-field spatial super-resolution.
{"title":"Light-field image super-resolution based on multi-scale feature fusion","authors":"Z. Yuanyuan, Shi Shengxian","doi":"10.12086/OEE.2020.200007","DOIUrl":"https://doi.org/10.12086/OEE.2020.200007","url":null,"abstract":"As a new generation of the imaging device, light-field camera can simultaneously capture the spatial position and incident angle of light rays. However, the recorded light-field has a trade-off between spatial resolution and angular resolution. Especially the application range of light-field cameras is restricted by the limited spatial resolution of sub-aperture images. Therefore, a light-field super-resolution neural network that fuses multi-scale features to obtain super-resolved light-field is proposed in this paper. The deep-learning-based network framework contains three major modules: multi-scale feature extraction, global feature fusion, and up-sampling. Firstly, inherent structural features in the 4D light-field are learned through the multi-scale feature extraction module, and then the fusion module is exploited for feature fusion and enhancement. Finally, the up-sampling module is used to achieve light-field super-resolution. The experimental results on the synthetic light-field dataset and real-world light-field dataset showed that this method outperforms other state-of-the-art methods in both visual and numerical evaluations. In addition, the super-resolved light-field images were applied to depth estimation in this paper, the results illustrated that the disparity map was enhanced through the light-field spatial super-resolution.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90241748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-22DOI: 10.12086/OEE.2020.200002
L. Xia, Gan Quan, Li Bing, Liu Xiao, Wang Bo
In order to solve the problems of sensitive initial contours and inaccurate segmentation caused by active contour segmentation of CT images, this paper proposes an automatic 3D vertebral CT active contour segmentation method combined weighted random forest called “WRF-AC”. This method proposes a weighted random forest algorithm and an active contour energy function that includes edge energy. First, the weighted random forest is trained by extracting 3D Haar-like feature values of the vertebra CT, and the 'vertebra center' obtained is used as the initial contour of the segmentation. Then, the segmentation of the vertebra CT image is completed by solving the active contour energy function minimum containing the edge energy. The experimental results show that this method can segment the spine CT images more accurately and quickly on the same datasets to extract the vertebrae.
{"title":"Automatic 3D vertebrae CT image active contour segmentation method based on weighted random forest","authors":"L. Xia, Gan Quan, Li Bing, Liu Xiao, Wang Bo","doi":"10.12086/OEE.2020.200002","DOIUrl":"https://doi.org/10.12086/OEE.2020.200002","url":null,"abstract":"In order to solve the problems of sensitive initial contours and inaccurate segmentation caused by active contour segmentation of CT images, this paper proposes an automatic 3D vertebral CT active contour segmentation method combined weighted random forest called “WRF-AC”. This method proposes a weighted random forest algorithm and an active contour energy function that includes edge energy. First, the weighted random forest is trained by extracting 3D Haar-like feature values of the vertebra CT, and the 'vertebra center' obtained is used as the initial contour of the segmentation. Then, the segmentation of the vertebra CT image is completed by solving the active contour energy function minimum containing the edge energy. The experimental results show that this method can segment the spine CT images more accurately and quickly on the same datasets to extract the vertebrae.","PeriodicalId":39552,"journal":{"name":"Guangdian Gongcheng/Opto-Electronic Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78855338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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