Pub Date : 2024-07-02DOI: 10.1109/JPHOT.2024.3421574
Jian Lin;Qiurong Yan;Quan Zou;Shida Sun;Zhen Wei;Hua Du
Single-pixel imaging is an advanced computational imaging technique based on compressive sensing that offers higher signal-to-noise ratio and broader application scope compared to traditional imaging techniques. However, conventional reconstruction algorithms suffer from issues such as long processing time and low reconstruction accuracy during the sampling and reconstruction processes. Deep learning-based compressed reconstruction networks can circumvent the complex iterative computations of traditional algorithms and achieve fast, high-quality reconstruction. In this paper, we propose a Variational Multi-Scale Error Compensation Network (VMSE) based on variational autoencoders. VMSE designs an error compensation network to enhance the feature representation capability of the sampling reconstruction network. We employ multiple latent variables to generate error features at different scales in the intermediate layers of the error compensation network, compensating the reconstructed image. Additionally, we design a module that simultaneously learns in the spatial and frequency domains, which is used for upsampling and complementing the missing high-frequency information in the frequency domain. On the MNIST dataset, when the sampling rate is 0.025, VMSE achieved higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) scores, especially with an SSIM score of 0.963, significantly surpassing Reconnet and DR2Net's scores of 0.930 and 0.920, respectively. This was further corroborated by practical experiments, where at low sampling rates, VMSE could reconstruct outlines more clearly compared to TVAL3.
{"title":"A Variational Multi-Scale Error Compensation Network for Single-Pixel Imaging","authors":"Jian Lin;Qiurong Yan;Quan Zou;Shida Sun;Zhen Wei;Hua Du","doi":"10.1109/JPHOT.2024.3421574","DOIUrl":"10.1109/JPHOT.2024.3421574","url":null,"abstract":"Single-pixel imaging is an advanced computational imaging technique based on compressive sensing that offers higher signal-to-noise ratio and broader application scope compared to traditional imaging techniques. However, conventional reconstruction algorithms suffer from issues such as long processing time and low reconstruction accuracy during the sampling and reconstruction processes. Deep learning-based compressed reconstruction networks can circumvent the complex iterative computations of traditional algorithms and achieve fast, high-quality reconstruction. In this paper, we propose a Variational Multi-Scale Error Compensation Network (VMSE) based on variational autoencoders. VMSE designs an error compensation network to enhance the feature representation capability of the sampling reconstruction network. We employ multiple latent variables to generate error features at different scales in the intermediate layers of the error compensation network, compensating the reconstructed image. Additionally, we design a module that simultaneously learns in the spatial and frequency domains, which is used for upsampling and complementing the missing high-frequency information in the frequency domain. On the MNIST dataset, when the sampling rate is 0.025, VMSE achieved higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) scores, especially with an SSIM score of 0.963, significantly surpassing Reconnet and DR2Net's scores of 0.930 and 0.920, respectively. This was further corroborated by practical experiments, where at low sampling rates, VMSE could reconstruct outlines more clearly compared to TVAL3.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 4","pages":"1-11"},"PeriodicalIF":2.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141514969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A transmission line (TL) impedance transformer of through-hole (TO)-CAN distributed feedback (DFB) laser is proposed and fabricated. The gain and noise factor (NF) of analog optical link can be improved by optimizing the laser impedance matching network. The radio frequency (RF) package of DFB is optimized to extend bandwidth and reduce return loss. In this paper, a flexible printed circuit (FPC) with low-loss impedance matching network is designed to improved the RF characteristics of TO-CAN DFB laser. The return path between FPC and TO-CAN is optimized to eliminate microwave resonances. The small signal model of an analog optical link is analyzed in detail. The measured frequency response of the TO-CAN DFB is 18.4 GHz. The microwave reflection is below −10 dB. The measured results correlates perfectly with the simulated results. The gain of analog optical link is increased by 3 dB. The NF is also reduced by about 2.5 dB.
{"title":"A TO-CAN DFB Laser With Transmission Line Impedance Transformer for Analog Optical Link","authors":"Congbiao Lei;Yuxuan Jiang;Guangcheng Zhong;Liang Xie","doi":"10.1109/JPHOT.2024.3422269","DOIUrl":"10.1109/JPHOT.2024.3422269","url":null,"abstract":"A transmission line (TL) impedance transformer of through-hole (TO)-CAN distributed feedback (DFB) laser is proposed and fabricated. The gain and noise factor (NF) of analog optical link can be improved by optimizing the laser impedance matching network. The radio frequency (RF) package of DFB is optimized to extend bandwidth and reduce return loss. In this paper, a flexible printed circuit (FPC) with low-loss impedance matching network is designed to improved the RF characteristics of TO-CAN DFB laser. The return path between FPC and TO-CAN is optimized to eliminate microwave resonances. The small signal model of an analog optical link is analyzed in detail. The measured frequency response of the TO-CAN DFB is 18.4 GHz. The microwave reflection is below −10 dB. The measured results correlates perfectly with the simulated results. The gain of analog optical link is increased by 3 dB. The NF is also reduced by about 2.5 dB.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 6","pages":"1-6"},"PeriodicalIF":2.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141514968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We report on versatile orbital angular momentum beam generation through the association of a 61-channels coherent beam combining digital laser and a liquid-crystal q-plate. Particularly, high-order vortex beams that carry orbital angular momentum are generated with radial, azimuthal and/or circular polarization states. The q-plate is designed and manufactured to sustain high average power ensuring that the vortex spatial mode is preserved. The proposed system offers an extra degree of freedom for various applications requesting beam shaping with specific polarization state.
{"title":"Controlled Generation of Orbital Angular Momentum Beams With Coherent Beam Combining Digital Laser and Liquid-Crystal q-Plate","authors":"Corentin Lechevalier;Claude-Alban Ranély-Vergé-Dépré;Ihsan Fsaifes;Rezki Becheker;Gerben Boer;Jean-Christophe Chanteloup","doi":"10.1109/JPHOT.2024.3421244","DOIUrl":"10.1109/JPHOT.2024.3421244","url":null,"abstract":"We report on versatile orbital angular momentum beam generation through the association of a 61-channels coherent beam combining digital laser and a liquid-crystal q-plate. Particularly, high-order vortex beams that carry orbital angular momentum are generated with radial, azimuthal and/or circular polarization states. The q-plate is designed and manufactured to sustain high average power ensuring that the vortex spatial mode is preserved. The proposed system offers an extra degree of freedom for various applications requesting beam shaping with specific polarization state.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 4","pages":"1-5"},"PeriodicalIF":2.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10578305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141514970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-01DOI: 10.1109/JPHOT.2024.3421275
Jun Li;Ruixu Yao
This work utilizes the CEEMDAN algorithm to analyze the interference of Rayleigh back-scattering signals in standard communication optical fibers. The technology has several advantages, such as anti-electromagnetic interference, improved electrical insulation, corrosion resistance, higher sensitivity, and the capability for long-distance monitoring. In this study, in-situ monitoring data from a 53.2 km natural gas pipeline in a terrain area in Southwest China were analyzed. The results demonstrate that, using the CEEMDAN algorithm for a blind test conducted over fourteen days, a 100% recognition accuracy for mechanical tamping and a Nuisance Alarm Rate (NAR) of less than 1% were achieved.
{"title":"Field Deployment of Natural Gas Pipeline Pre-Warning System With CEEMDAN Denoising Method","authors":"Jun Li;Ruixu Yao","doi":"10.1109/JPHOT.2024.3421275","DOIUrl":"10.1109/JPHOT.2024.3421275","url":null,"abstract":"This work utilizes the CEEMDAN algorithm to analyze the interference of Rayleigh back-scattering signals in standard communication optical fibers. The technology has several advantages, such as anti-electromagnetic interference, improved electrical insulation, corrosion resistance, higher sensitivity, and the capability for long-distance monitoring. In this study, in-situ monitoring data from a 53.2 km natural gas pipeline in a terrain area in Southwest China were analyzed. The results demonstrate that, using the CEEMDAN algorithm for a blind test conducted over fourteen days, a 100% recognition accuracy for mechanical tamping and a Nuisance Alarm Rate (NAR) of less than 1% were achieved.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 4","pages":"1-8"},"PeriodicalIF":2.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10578009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-28DOI: 10.1109/JPHOT.2024.3420787
Quan Zou;Qiurong Yan;Qianling Dai;Ao Wang;Bo Yang;Yi Li;Jinwei Yan
Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.
{"title":"Single Pixel Imaging Based on Multiple Prior Deep Unfolding Network","authors":"Quan Zou;Qiurong Yan;Qianling Dai;Ao Wang;Bo Yang;Yi Li;Jinwei Yan","doi":"10.1109/JPHOT.2024.3420787","DOIUrl":"10.1109/JPHOT.2024.3420787","url":null,"abstract":"Single-pixel imaging (SPI), an imaging technique based on the theory of compressed sensing, is limited in real-time imaging and high-resolution images due to its relatively slow imaging speed. In recent years, deep unfolding network compressed sensing reconstruction algorithms based on deep learning have proven to be an effective solution for faster and higher quality image reconstruction. However, existing deep unfolding networks mainly rely on a single piece of a priori information and may ignore other intrinsic structures of the image. Therefore, in this paper, we propose a deep unfolding network (MPDU-Net) that incorporates multiple prior information. To effectively fuse multiple prior information, we propose three different fusion strategies in the deep reconstruction sub-network. An unbiased convolutional layer is used to simulate the sampling reconstruction process to achieve joint reconstruction for effective removal of block artifacts. The sampling matrix is input into the deep reconstruction sub-network as a learnable parameter to achieve joint optimization of sampling reconstruction. Simulation and practical experimental results show that the proposed network outperforms existing compressed sensing reconstruction algorithms based on deep unfolding networks.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 4","pages":"1-10"},"PeriodicalIF":2.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-24DOI: 10.1109/JPHOT.2024.3418371
Wafaa M. R. Shakir;Ali S. Mahdi;Hani Hamdan;Jinan Charafeddine;Haitham Al Satai;Radouane Akrache;Samir Haddad;Jinane Sayah
In this article, we develop an innovative series representation for the sum of Rician non-zero boresight pointing error random variates based on the ${bm{kappa }} - {bm{mu }}$