Advanced Photonics Nexus最新文献_第3页

Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels 未经训练的神经网络提高了结构照明显微镜在强背景和噪声水平下的分辨率

Advanced Photonics Nexus Pub Date : 2023-07-01 DOI: 10.1117/1.APN.2.4.046005

Yuping He, Yunhua Yao, Yilin He, Zhen-Jian Huang, D. Qi, Chonglei Zhang, Xiaoshuai Huang, K. Shi, Pengpeng Ding, C. Jin, L. Deng, Zhenrong Sun, Xiaocong Yuan, Shian Zhang

{"title":"Untrained neural network enhances the resolution of structured illumination microscopy under strong background and noise levels","authors":"Yuping He, Yunhua Yao, Yilin He, Zhen-Jian Huang, D. Qi, Chonglei Zhang, Xiaoshuai Huang, K. Shi, Pengpeng Ding, C. Jin, L. Deng, Zhenrong Sun, Xiaocong Yuan, Shian Zhang","doi":"10.1117/1.APN.2.4.046005","DOIUrl":"https://doi.org/10.1117/1.APN.2.4.046005","url":null,"abstract":"Abstract. Structured illumination microscopy (SIM) has been widely applied in the superresolution imaging of subcellular dynamics in live cells. Higher spatial resolution is expected for the observation of finer structures. However, further increasing spatial resolution in SIM under the condition of strong background and noise levels remains challenging. Here, we report a method to achieve deep resolution enhancement of SIM by combining an untrained neural network with an alternating direction method of multipliers (ADMM) framework, i.e., ADMM-DRE-SIM. By exploiting the implicit image priors in the neural network and the Hessian prior in the ADMM framework associated with the optical transfer model of SIM, ADMM-DRE-SIM can further realize the spatial frequency extension without the requirement of training datasets. Moreover, an image degradation model containing the convolution with equivalent point spread function of SIM and additional background map is utilized to suppress the strong background while keeping the structure fidelity. Experimental results by imaging tubulins and actins show that ADMM-DRE-SIM can obtain the resolution enhancement by a factor of ∼1.6 compared to conventional SIM, evidencing the promising applications of ADMM-DRE-SIM in superresolution biomedical imaging.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123307658","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}

引用次数: 0

Experimental optical computing of complex vector convolution with twisted light 扭曲光复矢量卷积的实验光学计算

Advanced Photonics Nexus Pub Date : 2023-07-01 DOI: 10.1117/1.APN.2.4.046008

Ling Hong, Haoxu Guo, Xiaodong Qiu, Fei Lin, Wuhong Zhang, Lixiang Chen

引用次数: 0

Generation and control of extreme ultraviolet free-space optical skyrmions with high harmonic generation 高次谐波极紫外自由空间光学辐射的产生与控制

Advanced Photonics Nexus Pub Date : 2023-07-01 DOI: 10.1117/1.APN.2.4.046009

Yiqi Fang, Yunquan Liu

引用次数: 0

Complex-domain-enhancing neural network for large-scale coherent imaging 大规模相干成像的复杂域增强神经网络

Advanced Photonics Nexus Pub Date : 2023-07-01 DOI: 10.1117/1.APN.2.4.046006

Xuyang Chang, Rifa Zhao, Shaowei Jiang, Cheng Shen, G. Zheng, Changhuei Yang, Liheng Bian

{"title":"Complex-domain-enhancing neural network for large-scale coherent imaging","authors":"Xuyang Chang, Rifa Zhao, Shaowei Jiang, Cheng Shen, G. Zheng, Changhuei Yang, Liheng Bian","doi":"10.1117/1.APN.2.4.046006","DOIUrl":"https://doi.org/10.1117/1.APN.2.4.046006","url":null,"abstract":"Abstract. Large-scale computational imaging can provide remarkable space-bandwidth product that is beyond the limit of optical systems. In coherent imaging (CI), the joint reconstruction of amplitude and phase further expands the information throughput and sheds light on label-free observation of biological samples at micro- or even nano-levels. The existing large-scale CI techniques usually require scanning/modulation multiple times to guarantee measurement diversity and long exposure time to achieve a high signal-to-noise ratio. Such cumbersome procedures restrict clinical applications for rapid and low-phototoxicity cell imaging. In this work, a complex-domain-enhancing neural network for large-scale CI termed CI-CDNet is proposed for various large-scale CI modalities with satisfactory reconstruction quality and efficiency. CI-CDNet is able to exploit the latent coupling information between amplitude and phase (such as their same features), realizing multidimensional representations of the complex wavefront. The cross-field characterization framework empowers strong generalization and robustness for various coherent modalities, allowing high-quality and efficient imaging under extremely low exposure time and few data volume. We apply CI-CDNet in various large-scale CI modalities including Kramers–Kronig-relations holography, Fourier ptychographic microscopy, and lensless coded ptychography. A series of simulations and experiments validate that CI-CDNet can reduce exposure time and data volume by more than 1 order of magnitude. We further demonstrate that the high-quality reconstruction of CI-CDNet benefits the subsequent high-level semantic analysis.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"99 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123477548","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}

引用次数: 0

Digital subcarrier multiplexing-enabled carrier-free phase-retrieval receiver 支持数字子载波复用的无载波相位恢复接收机

Advanced Photonics Nexus Pub Date : 2023-06-09 DOI: 10.1117/1.APN.2.4.046004

Yunhe Ma, Meng Xiang, Wen Cheng, Ruitao Wu, Peijian Zhou, G. Zhou, Jilong Li, Jianping Li, S. Fu, Yuwen Qin

{"title":"Digital subcarrier multiplexing-enabled carrier-free phase-retrieval receiver","authors":"Yunhe Ma, Meng Xiang, Wen Cheng, Ruitao Wu, Peijian Zhou, G. Zhou, Jilong Li, Jianping Li, S. Fu, Yuwen Qin","doi":"10.1117/1.APN.2.4.046004","DOIUrl":"https://doi.org/10.1117/1.APN.2.4.046004","url":null,"abstract":"Abstract. The carrier-free phase-retrieval (CF-PR) receiver can reconstruct the optical field information only from two de-correlated intensity measurements without the involvement of a continuous-wave optical carrier. Here, we propose a digital subcarrier multiplexing (DSM)-enabled CF-PR receiver with hardware-efficient and modulation format-transparent merits. By numerically retrieving the optical field information of 56 GBaud DSM signals with QPSK/16QAM/32QAM modulation after 80-km standard single-mode fiber (SSMF) transmission, we identify that the DSM enabled CF-PR receiver is beneficial in reducing the implementation complexity of the CF-PR process, in comparison with the traditional single-carrier counterpart, because the lower symbol rate of each subcarrier is helpful in reducing the implementation complexity of multiple chromatic dispersion compensations and emulations during the PR iteration. Moreover, the DSM-enabled CF-PR receiver is verified to be robust toward various transmission imperfections, including transmitter-side laser linewidth and its wavelength drift, receiver-side time skew, and amplitude imbalance between two intensity tributaries. Finally, the superiority of the DSM-enabled CF-PR receiver is experimentally verified by recovering the optical field information of 25 GBaud 16QAM signals, after 40-km SSMF transmission for the first time. Thus, the DSM-enabled CF-PR receiver is promising for high-capacity photonic interconnection with direct detection.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"95 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129570984","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}

引用次数: 0

Complete active–passive photonic integration based on GaN-on-silicon platform 基于硅基氮化镓平台的完全有源无源光子集成

Advanced Photonics Nexus Pub Date : 2023-06-07 DOI: 10.1117/1.APN.2.4.046003

Jiabin Yan, Li Fang, Zhihang Sun, H. Zhang, Jia-lei Yuan, Yan Jiang, Yongjin Wang

引用次数: 0

Photoacoustic-enabled automatic vascular navigation: accurate and naked-eye real-time visualization of deep-seated vessels 光声自动血管导航:准确和肉眼实时可视化深层血管

Advanced Photonics Nexus Pub Date : 2023-05-13 DOI: 10.1117/1.APN.2.4.046001

Shu Pan, Li Wang, Yuanzheng Ma, Guangyu Zhang, R. Liu, Tao Zhang, K. Xiong, Siyu Chen, Jian Zhang, Wende Li, Sihua Yang

{"title":"Photoacoustic-enabled automatic vascular navigation: accurate and naked-eye real-time visualization of deep-seated vessels","authors":"Shu Pan, Li Wang, Yuanzheng Ma, Guangyu Zhang, R. Liu, Tao Zhang, K. Xiong, Siyu Chen, Jian Zhang, Wende Li, Sihua Yang","doi":"10.1117/1.APN.2.4.046001","DOIUrl":"https://doi.org/10.1117/1.APN.2.4.046001","url":null,"abstract":"Abstract. Accurate localization of blood vessels with image navigation is a key element in vascular-related medical research and vascular surgery. However, current vascular navigation techniques cannot provide naked-eye visualization of deep vascular information noninvasively and with high resolution, resulting in inaccurate vascular anatomy and diminished surgical success rates. Here, we introduce a photoacoustic-enabled automatic vascular navigation method combining photoacoustic computed tomography with augmented and mixed reality, for the first time, to our knowledge, enabling accurate and noninvasive visualization of the deep microvascular network within the tissues in real time on a real surgical surface. This approach achieves precise vascular localization accuracy (<0.89 mm) and tiny vascular relocation latency (<1 s) through a zero-mean normalization idea-based visual tracking algorithm and a curved surface-fitting algorithm. Further, the subcutaneous vessels of minimum diameter (∼0.15 mm) in rabbit thigh and the maximum depth (∼7 mm) in human arm can be vividly projected on the skin surface with a computer vision-based projection tracking system to simulate preoperative and intraoperative vascular localization. Thereby, this strategy provides a way to visualize deep vessels without damage on the surgical surface and with precise image navigation, opening an avenue for the application of photoacoustic imaging in surgical operations.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121763333","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}

引用次数: 1

Printable organic light-emitting diodes for next-generation visible light communications: a review 用于下一代可见光通信的可印刷有机发光二极管:综述

Advanced Photonics Nexus Pub Date : 2023-05-13 DOI: 10.1117/1.APN.2.4.044001

Kunping Guo, Zhenyu Tang, Xingxing Chou, Saihu Pan, Chun-Yan Wan, Tao Xue, Liping Ding, Xiao Wang, Jin Huang, Fanghui Zhang, Bin Wei

{"title":"Printable organic light-emitting diodes for next-generation visible light communications: a review","authors":"Kunping Guo, Zhenyu Tang, Xingxing Chou, Saihu Pan, Chun-Yan Wan, Tao Xue, Liping Ding, Xiao Wang, Jin Huang, Fanghui Zhang, Bin Wei","doi":"10.1117/1.APN.2.4.044001","DOIUrl":"https://doi.org/10.1117/1.APN.2.4.044001","url":null,"abstract":"Abstract. Visible light communication (VLC) is an emerging technology employing light-emitting diodes (LEDs) to provide illumination and wireless data transmission simultaneously. Harnessing cost-efficient printable organic LEDs (OLEDs) as environmentally friendly transmitters in VLC systems is extremely attractive for future applications in spectroscopy, the internet of things, sensing, and optical ranging in general. Here, we summarize the latest research progress on emerging semiconductor materials for LED sources in VLC, and highlight that OLEDs based on nontoxic and cost-efficient organic semiconductors have great opportunities for optical communication. We further examine efforts to achieve high-performance white OLEDs for general lighting, and, in particular, focus on the research status and opportunities for OLED-based VLC. Different solution-processable fabrication and printing strategies to develop high-performance OLEDs are also discussed. Finally, an outlook on future challenges and potential prospects of the next-generation organic VLC is provided.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129800852","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}

引用次数: 3

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder 深度图像先验加稀疏先验:用多阶延迟器实现单次全斯托克斯光谱偏振成像

Advanced Photonics Nexus Pub Date : 2023-05-01 DOI: 10.1117/1.APN.2.3.036009

Feng Han, Tingkui Mu, Haoyang Li, Abudusalamu Tuniyazi

{"title":"Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder","authors":"Feng Han, Tingkui Mu, Haoyang Li, Abudusalamu Tuniyazi","doi":"10.1117/1.APN.2.3.036009","DOIUrl":"https://doi.org/10.1117/1.APN.2.3.036009","url":null,"abstract":"Abstract. Compressive full-Stokes spectropolarimetric imaging (SPI), integrating passive polarization modulator (PM) into general imaging spectrometer, is powerful enough to capture high-dimensional information via incomplete measurement; a reconstruction algorithm is needed to recover 3D data cube (x, y, and λ) for each Stokes parameter. However, existing PMs usually consist of complex elements and enslave to accurate polarization calibration, current algorithms suffer from poor imaging quality and are subject to noise perturbation. In this work, we present a single multiple-order retarder followed a polarizer to implement passive spectropolarimetric modulation. After building a unified forward imaging model for SPI, we propose a deep image prior plus sparsity prior algorithm for high-quality reconstruction. The method based on untrained network does not need training data or accurate polarization calibration and can simultaneously reconstruct the 3D data cube and achieve self-calibration. Furthermore, we integrate the simplest PM into our miniature snapshot imaging spectrometer to form a single-shot SPI prototype. Both simulations and experiments verify the feasibility and outperformance of our SPI scheme. It provides a paradigm that allows general spectral imaging systems to become passive full-Stokes SPI systems by integrating the simplest PM without changing their intrinsic mechanism.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116071470","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}

引用次数: 1

Fringe-pattern analysis with ensemble deep learning 基于集成深度学习的条纹模式分析

Advanced Photonics Nexus Pub Date : 2023-05-01 DOI: 10.1117/1.APN.2.3.036010

Shijie Feng, Yile Xiao, Wei Yin, Yan Hu, Yixuan Li, C. Zuo, Qian Chen

{"title":"Fringe-pattern analysis with ensemble deep learning","authors":"Shijie Feng, Yile Xiao, Wei Yin, Yan Hu, Yixuan Li, C. Zuo, Qian Chen","doi":"10.1117/1.APN.2.3.036010","DOIUrl":"https://doi.org/10.1117/1.APN.2.3.036010","url":null,"abstract":"Abstract. In recent years, there has been tremendous progress in the development of deep-learning-based approaches for optical metrology, which introduce various deep neural networks (DNNs) for many optical metrology tasks, such as fringe analysis, phase unwrapping, and digital image correlation. However, since different DNN models have their own strengths and limitations, it is difficult for a single DNN to make reliable predictions under all possible scenarios. In this work, we introduce ensemble learning into optical metrology, which combines the predictions of multiple DNNs to significantly enhance the accuracy and reduce the generalization error for the task of fringe-pattern analysis. First, several state-of-the-art base models of different architectures are selected. A K-fold average ensemble strategy is developed to train each base model multiple times with different data and calculate the mean prediction within each base model. Next, an adaptive ensemble strategy is presented to further combine the base models by building an extra DNN to fuse the features extracted from these mean predictions in an adaptive and fully automatic way. Experimental results demonstrate that ensemble learning could attain superior performance over state-of-the-art solutions, including both classic and conventional single-DNN-based methods. Our work suggests that by resorting to collective wisdom, ensemble learning offers a simple and effective solution for overcoming generalization challenges and boosts the performance of data-driven optical metrology methods.","PeriodicalId":223078,"journal":{"name":"Advanced Photonics Nexus","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804194","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}

引用次数: 1