In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.
{"title":"Spatial light interference microscopy: principle and applications to biomedicine.","authors":"Xi Chen, Mikhail E Kandel, Gabriel Popescu","doi":"10.1364/AOP.417837","DOIUrl":"https://doi.org/10.1364/AOP.417837","url":null,"abstract":"<p><p>In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.</p>","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":"13 2","pages":"353-425"},"PeriodicalIF":27.1,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048520/pdf/nihms-1764836.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9612727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrated phase-sensitive photonic sensors: A system design tutorial","authors":"Johannes Milvich, D. Kohler, W. Freude, C. Koos","doi":"10.1364/aop.413399","DOIUrl":"https://doi.org/10.1364/aop.413399","url":null,"abstract":"","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":" ","pages":""},"PeriodicalIF":27.1,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48628064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fano resonance for applications","authors":"M. Limonov","doi":"10.1364/AOP.420731","DOIUrl":"https://doi.org/10.1364/AOP.420731","url":null,"abstract":"","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":" ","pages":""},"PeriodicalIF":27.1,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41643493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nanophotonics is an important branch of modern optics dealing with lightmatter interaction at the nanoscale. Nanoparticles can exhibit enhanced light absorption under illumination by light, and they become nanoscale sources of heat that can be precisely controlled and manipulated. For metal nanoparticles, such effects have been studied in the framework of thermoplasmonics which, similar to plasmonics itself, has a number of limitations. Recently emerged all-dielectric resonant nanophotonics is associated with optically-induced electric and magnetic Mie resonances, and this field is developing very rapidly in the last decade. As a result, thermoplasmonics is being replaced by all-dielectric thermonanophotonics with many important applications such as photothermal cancer therapy, drug and gene delivery, nanochemistry, and photothermal imaging. This review paper aims to introduce this new field of non-plasmonic nanophotonics and discuss associated thermally-induced processes at the nanoscale. 1 ar X iv :2 10 4. 01 96 4v 1 [ ph ys ic s. op tic s] 5 A pr 2 02 1
纳米光子学是现代光学的一个重要分支，研究纳米级的光物质相互作用。纳米颗粒在光照下可以表现出增强的光吸收，并且它们成为可以精确控制和操纵的纳米级热源。对于金属纳米颗粒，这种效应是在热等离子体的框架下研究的，与等离子体本身类似，热等离子体有很多局限性。最近出现的全介电共振纳米光子学与光学诱导的电和磁Mie共振有关，并且该领域在过去十年中发展非常迅速。因此，热等离子体正被全介电热纳米光子学所取代，其具有许多重要应用，如光热癌症治疗、药物和基因递送、纳米化学和光热成像。本文旨在介绍非等离子体纳米光子学的这一新领域，并讨论纳米尺度上相关的热诱导过程。1 ar X iv：2 10 4。01 96 4v 1[ph ys ic s.op tics]5 A pr 2 02 1
{"title":"All-dielectric thermonanophotonics","authors":"G. Zograf, M. Petrov, S. Makarov, Y. Kivshar","doi":"10.1364/AOP.426047","DOIUrl":"https://doi.org/10.1364/AOP.426047","url":null,"abstract":"Nanophotonics is an important branch of modern optics dealing with lightmatter interaction at the nanoscale. Nanoparticles can exhibit enhanced light absorption under illumination by light, and they become nanoscale sources of heat that can be precisely controlled and manipulated. For metal nanoparticles, such effects have been studied in the framework of thermoplasmonics which, similar to plasmonics itself, has a number of limitations. Recently emerged all-dielectric resonant nanophotonics is associated with optically-induced electric and magnetic Mie resonances, and this field is developing very rapidly in the last decade. As a result, thermoplasmonics is being replaced by all-dielectric thermonanophotonics with many important applications such as photothermal cancer therapy, drug and gene delivery, nanochemistry, and photothermal imaging. This review paper aims to introduce this new field of non-plasmonic nanophotonics and discuss associated thermally-induced processes at the nanoscale. 1 ar X iv :2 10 4. 01 96 4v 1 [ ph ys ic s. op tic s] 5 A pr 2 02 1","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":" ","pages":""},"PeriodicalIF":27.1,"publicationDate":"2021-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48139794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Di Zhu, Linbo Shao, Mengjie Yu, Rebecca Cheng, B. Desiatov, C. Xin, Yaowen Hu, Jeffrey Holzgrafe, S. Ghosh, A. Shams-Ansari, Eric Puma, N. Sinclair, C. Reimer, Mian Zhang, M. Lončar
Lithium niobate (LN), an outstanding and versatile material, has influenced our daily life for decades—from enabling high-speed optical communications that form the backbone of the Internet to realizing radio-frequency filtering used in our cell phones. This half-century-old material is currently embracing a revolution in thin-film LN integrated photonics. The successes of manufacturing wafer-scale, high-quality thin films of LN-on-insulator (LNOI) and breakthroughs in nanofabrication techniques have made high-performance integrated nanophotonic components possible. With rapid development in the past few years, some of these thin-film LN devices, such as optical modulators and nonlinear wavelength converters, have already outperformed their legacy counterparts realized in bulk LN crystals. Furthermore, the nanophotonic integration has enabled ultra-low-loss resonators in LN, which has unlocked many novel applications such as optical frequency combs and quantum transducers. In this review, we cover—from basic principles to the state of the art—the diverse aspects of integrated thin-film LN photonics, including the materials, basic passive components, and various active devices based on electro-optics, all-optical nonlinearities, and acousto-optics. We also identify challenges that this platform is currently facing and point out future opportunities. The field of integrated LNOI photonics is advancing rapidly and poised to make critical impacts on a broad range of applications in communication, signal processing, and quantum information.
{"title":"Integrated photonics on thin-film lithium niobate","authors":"Di Zhu, Linbo Shao, Mengjie Yu, Rebecca Cheng, B. Desiatov, C. Xin, Yaowen Hu, Jeffrey Holzgrafe, S. Ghosh, A. Shams-Ansari, Eric Puma, N. Sinclair, C. Reimer, Mian Zhang, M. Lončar","doi":"10.1364/AOP.411024","DOIUrl":"https://doi.org/10.1364/AOP.411024","url":null,"abstract":"Lithium niobate (LN), an outstanding and versatile material, has influenced our daily life for decades—from enabling high-speed optical communications that form the backbone of the Internet to realizing radio-frequency filtering used in our cell phones. This half-century-old material is currently embracing a revolution in thin-film LN integrated photonics. The successes of manufacturing wafer-scale, high-quality thin films of LN-on-insulator (LNOI) and breakthroughs in nanofabrication techniques have made high-performance integrated nanophotonic components possible. With rapid development in the past few years, some of these thin-film LN devices, such as optical modulators and nonlinear wavelength converters, have already outperformed their legacy counterparts realized in bulk LN crystals. Furthermore, the nanophotonic integration has enabled ultra-low-loss resonators in LN, which has unlocked many novel applications such as optical frequency combs and quantum transducers. In this review, we cover—from basic principles to the state of the art—the diverse aspects of integrated thin-film LN photonics, including the materials, basic passive components, and various active devices based on electro-optics, all-optical nonlinearities, and acousto-optics. We also identify challenges that this platform is currently facing and point out future opportunities. The field of integrated LNOI photonics is advancing rapidly and poised to make critical impacts on a broad range of applications in communication, signal processing, and quantum information.","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":"13 1","pages":"242"},"PeriodicalIF":27.1,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47742892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We review the impact of deep-learning technologies on camera architecture. The function of a camera is first to capture visual information and second to form an image. Conventionally, both functions are implemented in physical optics. Throughout the digital age, however, joint design of physical sampling and electronic processing, e.g., computational imaging, has been increasingly applied to improve these functions. Over the past five years, deep learning has radically improved the capacity of computational imaging. Here we briefly review the development of artificial neural networks and their recent intersection with computational imaging. We then consider in more detail how deep learning impacts the primary strategies of computational photography: focal plane modulation, lens design, and robotic control. With focal plane modulation, we show that deep learning improves signal inference to enable faster hyperspectral, polarization, and video capture while reducing the power per pixel by 10−100×. With lens design, deep learning improves multiple aperture image fusion to enable task-specific array cameras. With control, deep learning enables dynamic scene-specific control that may ultimately enable cameras that capture the entire optical data cube (the “light field”), rather than just a focal slice. Finally, we discuss how these three strategies impact the physical camera design as we seek to balance physical compactness and simplicity, information capacity, computational complexity, and visual fidelity.
{"title":"Deep learning for camera data acquisition, control, and image estimation","authors":"D. Brady, Lu Fang, Zhan Ma","doi":"10.1364/AOP.398263","DOIUrl":"https://doi.org/10.1364/AOP.398263","url":null,"abstract":"We review the impact of deep-learning technologies on camera architecture. The function of a camera is first to capture visual information and second to form an image. Conventionally, both functions are implemented in physical optics. Throughout the digital age, however, joint design of physical sampling and electronic processing, e.g., computational imaging, has been increasingly applied to improve these functions. Over the past five years, deep learning has radically improved the capacity of computational imaging. Here we briefly review the development of artificial neural networks and their recent intersection with computational imaging. We then consider in more detail how deep learning impacts the primary strategies of computational photography: focal plane modulation, lens design, and robotic control. With focal plane modulation, we show that deep learning improves signal inference to enable faster hyperspectral, polarization, and video capture while reducing the power per pixel by 10−100×. With lens design, deep learning improves multiple aperture image fusion to enable task-specific array cameras. With control, deep learning enables dynamic scene-specific control that may ultimately enable cameras that capture the entire optical data cube (the “light field”), rather than just a focal slice. Finally, we discuss how these three strategies impact the physical camera design as we seek to balance physical compactness and simplicity, information capacity, computational complexity, and visual fidelity.","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":"12 1","pages":"787-846"},"PeriodicalIF":27.1,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49153253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The transistor has revolutionized civilization. The photon will enable the next revolution provided that photomechanical materials, which convert light energy into mechanical work, can be made substantially more efficient. This tutorial develops a unified picture of the photomechanical response from its microscopic origins to the bulk response. A statistical model of the relationship between the photomorphon, the smallest photomechanical material unit, and the bulk response provides the context for understanding the various mechanisms that can contribute. We then present experimental details of how the photomechanical response is measured and used to deduce the underlying mechanisms. A figure of merit for the photomechanical efficiency is defined and materials are reviewed. Finally, we describe the photomechanical optical device (POD) and how PODs can be combined to form highly intelligent materials. This tutorial spans the multidisciplinary topics needed to (1) understand the fundamental physics of the response, (2) design and process materials to control the response, and (3) build new devices and integrated photomechanical systems.
{"title":"Photomechanical materials and applications: a tutorial","authors":"M. Kuzyk, Nathan J. Dawson","doi":"10.1364/AOP.387366","DOIUrl":"https://doi.org/10.1364/AOP.387366","url":null,"abstract":"The transistor has revolutionized civilization. The photon will enable the next revolution provided that photomechanical materials, which convert light energy into mechanical work, can be made substantially more efficient. This tutorial develops a unified picture of the photomechanical response from its microscopic origins to the bulk response. A statistical model of the relationship between the photomorphon, the smallest photomechanical material unit, and the bulk response provides the context for understanding the various mechanisms that can contribute. We then present experimental details of how the photomechanical response is measured and used to deduce the underlying mechanisms. A figure of merit for the photomechanical efficiency is defined and materials are reviewed. Finally, we describe the photomechanical optical device (POD) and how PODs can be combined to form highly intelligent materials. This tutorial spans the multidisciplinary topics needed to (1) understand the fundamental physics of the response, (2) design and process materials to control the response, and (3) build new devices and integrated photomechanical systems.","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":"12 1","pages":"847-1011"},"PeriodicalIF":27.1,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66410125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Javidi, F. Pla, J. Sotoca, Xin Shen, P. Latorre-Carmona, M. Martínez-Corral, R. Fernández-Beltran, G. Krishnan
Automated human gesture recognition is receiving significant research interest, with applications ranging from novel acquisition techniques to algorithms, data processing, and classification methodologies. This tutorial presents an overview of the fundamental components and basics of the current 3D optical image acquisition technologies for gesture recognition, including the most promising algorithms. Experimental results illustrate some examples of 3D integral imaging, which are compared to conventional 2D optical imaging. Examples of classifying human gestures under normal and degraded conditions, such as low illumination and the presence of partial occlusions, are provided. This tutorial is aimed at an audience who may or may not be familiar with gesture recognition approaches, current 3D optical image acquisition techniques, and classification algorithms and methodologies applied to human gesture recognition.
{"title":"Fundamentals of automated human gesture recognition using 3D integral imaging: a tutorial","authors":"B. Javidi, F. Pla, J. Sotoca, Xin Shen, P. Latorre-Carmona, M. Martínez-Corral, R. Fernández-Beltran, G. Krishnan","doi":"10.1364/aop.390929","DOIUrl":"https://doi.org/10.1364/aop.390929","url":null,"abstract":"Automated human gesture recognition is receiving significant research interest, with applications ranging from novel acquisition techniques to algorithms, data processing, and classification methodologies. This tutorial presents an overview of the fundamental components and basics of the current 3D optical image acquisition technologies for gesture recognition, including the most promising algorithms. Experimental results illustrate some examples of 3D integral imaging, which are compared to conventional 2D optical imaging. Examples of classifying human gestures under normal and degraded conditions, such as low illumination and the presence of partial occlusions, are provided. This tutorial is aimed at an audience who may or may not be familiar with gesture recognition approaches, current 3D optical image acquisition techniques, and classification algorithms and methodologies applied to human gesture recognition.","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":"12 1","pages":"1237-1299"},"PeriodicalIF":27.1,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44463819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin C Zhou, Ruobing Qian, Al-Hafeez Z. Dhalla, Sina Farsiu, J. Izatt
We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D k-space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D k-space representation of the sample. While only the axial dimension of OCT is typically analyzed in k-space, we show that embracing a fully 3D k-space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel-beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory not only enables clear understanding of existing techniques, but also suggests new research directions to continue advancing the field of OCT.
{"title":"Unified k-space theory of optical coherence tomography","authors":"Kevin C Zhou, Ruobing Qian, Al-Hafeez Z. Dhalla, Sina Farsiu, J. Izatt","doi":"10.1364/AOP.417102","DOIUrl":"https://doi.org/10.1364/AOP.417102","url":null,"abstract":"We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D k-space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D k-space representation of the sample. While only the axial dimension of OCT is typically analyzed in k-space, we show that embracing a fully 3D k-space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel-beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory not only enables clear understanding of existing techniques, but also suggests new research directions to continue advancing the field of OCT.","PeriodicalId":48960,"journal":{"name":"Advances in Optics and Photonics","volume":" ","pages":""},"PeriodicalIF":27.1,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48018316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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