SPIE/COS Photonics Asia最新文献_第9页

End-to-end metasurface inverse design for NIR computational imaging 用于近红外计算成像的端到端元表面反设计

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2687265

Chang Wang, Qiangbo Zhang, Xinyu Liu, Yang Zhang, Zhou Xu, Zhenrong Zheng

引用次数: 0

Holographic laser-driven light sources for computational imaging 用于计算成像的全息激光驱动光源

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2686667

K. Kumagai, Hsin-hui Huang, Koji Hatanaka, Y. Hayasaki

引用次数: 0

High-sensitivity Goos-Hänchen shifts sensor in twisted bilayer graphene on hexagonal boron nitride 六方氮化硼上扭曲双层石墨烯中的高灵敏度 Goos-Hänchen 漂移传感器

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2686933

Siying Huang, Leyong Jiang, Zhiwei Zheng

引用次数: 0

Measurement of materials refractive indexs based on laser self-mixing interferometry and convolutional neural network 基于激光自混合干涉仪和卷积神经网络的材料折射率测量技术

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2687384

Jinyuan Chen, Junwei H. Xu, Bin Liu

引用次数: 0

High speed laser-assisted copper microstructures synthesis from deep eutectic solutions 从深共晶溶液中高速合成激光辅助铜微结构

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2690631

L. Logunov, Ildar Yusupov, Dmitriy Shestakov, Andrey Shishov, Ilya Tumkin

引用次数: 0

The generation of single-cavity triple-comb pulses from a wavelength/polarization multiplexed fiber laser 从波长/偏振多路光纤激光器中产生单腔三重炸弹脉冲

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2687275

Ying Qin, Guoqing Hu, Tengfei Wu, Guangwei Chen, Zhehai Zhou, Li Chen, Pengyu Yan, Yue Zhao, Xiaochun Liao, Hetian Li

引用次数: 0

Deep learning-assisted slightly off-axis digital holographic quantitative phase imaging 深度学习辅助轻微离轴数字全息定量相位成像

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2688338

Zhuoshi Li, C. Zuo, Qian Chen

{"title":"Deep learning-assisted slightly off-axis digital holographic quantitative phase imaging","authors":"Zhuoshi Li, C. Zuo, Qian Chen","doi":"10.1117/12.2688338","DOIUrl":"https://doi.org/10.1117/12.2688338","url":null,"abstract":"Digital holographic microscopy (DHM) is a typical quantitative phase imaging technique, in which the entire complex wavefront information is interferometrically encoded as a fringe pattern (so-called hologram) and then quantitatively demodulated by fringe analysis methods. Yet the off-axis digital holographic phase demodulation typically requires sufficiently high carrier spatial frequency for separating ±1-order and 0-order spectrum in the Fourier domain, limiting the space-bandwidth product (SBP) of the system. The in-line holographic configuration can realize full detector-bandwidth phase reconstruction at the cost of time resolution. In this work, we proposed a high-accuracy artifacts-free single-frame low-carrier frequency fringe demodulation scheme for the slightly off-axis digital holography, optimizing the system’s SBP effectively. This scheme acts as a method of deep-learning assisted physical model, incorporating a convolution neural network into a complete physical model by the idea of residual compensation, which enhances the imaging precision of the physical method while promises the interpretability of deep learning. The effectiveness of the proposed method is quantitatively analyzed through numerical simulation and experimentally verified by live-cells experiment. The phase recovered accuracy can be improved by one order of magnitude (the MAE up to 0.0065) compared with the traditional physical method. The live-cells experiment demonstrates the practicality of the method in biological research. Furthermore, it’s worth noting that the proposed method achieves a higher reconstruction accuracy utilizing only a small fraction of the datasets of the classical end-to-end deep learning model (without a physical model). The proposed deep learning-assisted physical model idea in this article is expected to bring more solutions for diverse computational imaging techniques.","PeriodicalId":149506,"journal":{"name":"SPIE/COS Photonics Asia","volume":"59 1","pages":"127680X - 127680X-7"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228489","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

Multiwavelength focused ultrasound-optics system for measuring concentration changes in the focused area 用于测量聚焦区域浓度变化的多波长聚焦超声光学系统

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2687681

Triana M. Rahayu, Mohammadreza Omidali, Zuomin Zhao, T. Myllylä

{"title":"Multiwavelength focused ultrasound-optics system for measuring concentration changes in the focused area","authors":"Triana M. Rahayu, Mohammadreza Omidali, Zuomin Zhao, T. Myllylä","doi":"10.1117/12.2687681","DOIUrl":"https://doi.org/10.1117/12.2687681","url":null,"abstract":"The multiwavelength focused ultrasound (FUS)-optic technique in development aims to provide application for spatially accurate measurements of tissue properties in the focus area in real-time. But setting up such a system is quite challenging due to several factors, for instance the integration of different optical sources, positioning of the optical and acoustic beams on the phantom, and synchronization. In this presentation, we present first steps towards designing a multiwavelength FUS-optic system that measures wavelength dependent changes in the focus area. As a test substance, we used intralipid solution, milk, and the mixture with different levels of concentration. The expected signal peak heights of tagged photons intensities of different wavelengths are partly dependent on the concentration due to the wavelength-dependent optical absorbance. Furthermore, detected FUS modulated amplitudes are in relation with the concentration of sample in focus area. The aim of this study is to characterize the relationship between FUS-optic signal peak heights and the different levels of sample concentration at different wavelengths.","PeriodicalId":149506,"journal":{"name":"SPIE/COS Photonics Asia","volume":"21 1","pages":"1277018 - 1277018-3"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139228764","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

Grade classification of Alzheimer's disease using hyperspectral ophthalmoscope images 利用高光谱眼底镜图像对阿尔茨海默病进行分级

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2688532

H. Nguyen, Y. Tsao, Songcun Lu, Hsiang-Chen Wang

{"title":"Grade classification of Alzheimer's disease using hyperspectral ophthalmoscope images","authors":"H. Nguyen, Y. Tsao, Songcun Lu, Hsiang-Chen Wang","doi":"10.1117/12.2688532","DOIUrl":"https://doi.org/10.1117/12.2688532","url":null,"abstract":"Detecting Alzheimer’s disease presents considerable challenges in the medical field. Hyperspectral imaging offers a promising approach to identifying the disease by analyzing spectral information from the diseased retina. This study utilized 3,256 ophthalmoscopic images from 137 patients and applied hyperspectral imaging technology to capture spectral data from specific regions within these images. Deep learning models, including ResNet50, Inception v3, GoogLeNet, and EfficientNet, were introduced and evaluated for lesion detection accuracy. In the training phase, these artificial neural networks showed varying accuracies in lesion detection using both original and hyperspectral images. For instance, ResNet50 achieved 80% accuracy with original images and 84% with hyperspectral images, while Inception v3 consistently achieved 80% accuracy with both types of images. GoogLeNet demonstrated 81% accuracy with original images, improving to 83% with hyperspectral images, and EfficientNet recorded accuracies of 80% for original images and 82% for hyperspectral images. The combination of hyperspectral imaging and deep learning models shows promise in enhancing Alzheimer’s disease detection through ophthalmoscopic image analysis, with hyperspectral images exhibiting higher efficacy compared to original images.","PeriodicalId":149506,"journal":{"name":"SPIE/COS Photonics Asia","volume":"1 1","pages":"1276707 - 1276707-10"},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229020","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

Uncertainty on Brillouin scattering measurements on bulk materials using a power laser 使用功率激光对块状材料进行布里渊散射测量的不确定性

SPIE/COS Photonics Asia Pub Date : 2023-11-27 DOI: 10.1117/12.2689285

Patrice Salzenstein, Thomas Y. Wu

引用次数: 0