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Physics-informed deep learning for fringe pattern analysis

Opto-Electronic Advances 2024年第1期7卷 4-15页

作者： Wei yin yuxuan Che Xinsheng Li Mingyu Li yan Hu Shijie Feng edmund y.lam Qian Chen Chao Zuo Smart Computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjing 210094China Smart Computational Imaging Research Institute(SCIRI)of Nanjing University of Science and Technology Nanjing 210019China Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense Nanjing 210094China Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong Kong SAR 999077China

Recently,deep learning has yielded transformative success across optics and photonics,especially in optical *** neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe ***,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning *** this end,we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (Le FTP) *** parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning *** by the initial phase from Le FTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end *** results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during *** proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

关键词： optical metrology deep learning physics-informed neural networks fringe analysis phase retrieval

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On the use of deep learning for phase recovery

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Light(Science & Applications) 2024年第2期13卷 190-235页

作者： Kaiqiang Wang Li Song Chutian Wang Zhenbo Ren Guangyuan Zhao Jiazhen Dou Jianglei Di George Barbastathis Renjie Zhou Jianlin Zhao edmund y.lam Department of Electrical and Electronic Engineering The University of Hong KongHong Kong SARChina School of Physical Science and Technology Northwestern Polytechnical UniversityXi’anChina Department of Biomedical Engineering The Chinese University of Hong KongHong Kong SARChina School of Information Engineering Guangdong University of TechnologyGuangzhouChina Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMAUSA

Phase recovery(PR)refers to calculating the phase of the light field from its intensity *** exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics,PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging *** recent years,deep learning(DL),often implemented through deep neural networks,has provided unprecedented support for computational imaging,leading to more efficient solutions for various PR *** this review,we first briefly introduce conventional methods for ***,we review how DL provides support for PR from the following three stages,namely,pre-processing,in-processing,and *** also review how DL is used in phase image ***,we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of ***,we present a live-updating resource(https://***/kqwang/phase-recovery)for readers to learn more about PR.

关键词： networks phase optics

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Resolution enhancement of optical scanning holography with a spiral modulated point spread function

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Photonics Research 2016年第1期4卷 1-6页

作者： Ni Chen Zhenbo Ren Haiyan Ou edmund y.lam Department of Electrical and Electronic Engineering The University of Hong Kong Institute of Applied Physics University of Electronic Science and Technology of China

In optical scanning holography, one pupil produces a spherical wave and another produces a plane wave. They interfere with each other and result in a fringe pattern for scanning a three-dimensional object. The resolution of the hologram reconstruction is affected by the point spread function(PSF) of the optical system. In this paper, we modulate the PSF by a spiral phase plate, which significantly enhances the lateral and depth resolution. We explain the theory for such resolution enhancement and show simulation results to verify the efficacy of the approach.

关键词： PSF Resolution enhancement of optical scanning holography with a spiral modulated point spread function

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Fast and robust phase retrieval for masked coherent diffractive imaging

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Photonics Research 2022年第3期10卷 758-768页

作者： Li Song edmund y.lam Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong KongChina

Conventional phase retrieval algorithms for coherent diffractive imaging(CDI)require many iterations to deliver reasonable results,even using a known mask as a strong constraint in the imaging setup,an approach known as masked *** paper proposes a fast and robust phase retrieval method for masked CDI based on the alternating direction method of multipliers(ADMM).We propose a plug-and-play ADMM to incorporate the prior knowledge of the mask,but note that commonly used denoisers are not suitable as regularizers for complex-valued latent images ***,we develop a regularizer based on the structure tensor and Harris corner *** with conventional phase retrieval methods,our technique can achieve comparable reconstruction results with less time for the masked ***,validation experiments on real in situ CDI data for both intensity and phase objects show that our approach is more than 100 times faster than the baseline method to reconstruct one complex-valued image,making it possible to be used in challenging situations,such as imaging dynamic ***,phase retrieval results for single diffraction patterns show the robustness of the proposed ADMM.

关键词： coherent phase retrieval

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Unsupervised learning on scientific ocean drilling datasets from the South China Sea

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Frontiers of Earth Science 2019年第1期13卷 180-190页

作者： Kevin C.TSE Hon-Chim CHIU Man-yin TSANG yiliang LI edmund y.lam Department of Earth Sciences The University of Hong KongPokfulamHong KongChina Department of Geography and Centre for Geo-computation Studies Hong Kong Baptist UniversityKowloon TongHong KongChina Department of Earth Sciences University of TorontoTorontoON M5S 2M8Canada Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong KongChina

Unsupervised learning methods were applied to explore data patterns in multivariate geophysical datasets collected from ocean floor sediment core samples coming from scientific ocean drilling in the South China *** to studies on similar datasets,but using supervised learning methods which are designed to make predictions based on sample training data,unsupervised learning methods require no a priori information and focus only on the input *** this study,popular unsupervised learning methods including K-means,self-organizing maps,hierarchical clustering and random forest were coupled with different distance metrics to form exploratory data *** resulting data clusters were externally validated with lithologic units and geologic time scales assigned to the datasets by conventional methods. Compact and connected data clusters displayed varying degrees of correspondence with existing classification by lithologic units and geologic time scales.K-means and self-organizing maps were observed to perform better with lithologic units while random forest corresponded best with geologic time *** study sets a pioneering example of how unsupervised machine learning methods can be used as an automatic processing tool for the increasingly high volume of scientific ocean drilling data.

关键词： machine learning unsupervised learning ODP IODP clustering

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Enhanced depth resolution in optical scanning holography using a configurable pupil

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Photonics Research 2014年第2期2卷 64-70页

作者： Haiyan Ou Ting-Chung Poon Kenneth K.y.Wong edmund y.lam Department of Electrical and Electronic Engineering University of Hong KongPokfulamHong KongChina Institute of Applied Physics University of Electronic Science and Technology of China610054ChengduChina Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVirginia 24061USA

The optical scanning holography(OSH)technique can capture all the three-dimensional volume information of an object in a hologram via a single raster *** digital hologram can then be processed to reconstruct individual sectional images of the *** this paper,we present a scheme to reconstruct sectional images in OSH with enhanced depth resolution,where a spatial light modulator(SLM)is adopted as a configurable point *** switching the SLM between two states,different Fresnel zone plates(FZPs)are generated based on the same optical *** extra information provided by different FZPs,a depth resolution at 0.7μm can be achieved.

关键词： resolution sectional Fresnel

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光学扫描全息术研究进展

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光学学报 2020年第1期40卷 146-156页

作者：任振波林彥民西北工业大学物理科学与技术学院超常条件材料物理与化学教育部重点实验室陕西省光信息技术重点实验室陕西西安710129 香港大学工程学院电机与电子工程系香港

光学成像技术极大地拓展了人类的视觉极限,提高了人们观察和理解现实世界的能力。越多地获得目标的光学信息,对其的认识越充分。数字全息术是一种可以将样本的三维信息以二维全息图的形式编码记录下来的一种成像技术。通过获得由携带物... 详细信息

光学成像技术极大地拓展了人类的视觉极限,提高了人们观察和理解现实世界的能力。越多地获得目标的光学信息,对其的认识越充分。数字全息术是一种可以将样本的三维信息以二维全息图的形式编码记录下来的一种成像技术。通过获得由携带物体信息的物光波和参考光波叠加产生的干涉图案,可以以数字化的方式实现多种重建模态,例如图像恢复、相位成像和切片成像等。光学扫描全息术是一种独特的数字全息成像技术,通过主动式二维化扫描对三维物体进行成像,其完整的波前信息可以被单像素探测器记录,并基于光外差检测进行信号解调,从而恢复出复数全息图。对光学扫描全息术的最新进展进行介绍。首先,基于双光瞳成像系统,通过特殊的硬件和算法设计,提高光学成像系统的性能,如提高空间分辨率、缩短扫描时间。其次,基于计算成像原理,通过改进和优化全息像重建算法,实现高质量的图像恢复,主要涉及切片成像和三维成像等重建模态。第三,介绍光学扫描全息术的其他研究方向,并讨论该领域未来可能的发展方向。

关键词：成像系统数字全息术光学扫描全息术计算成像图像重建

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Hybrid brightfield and darkfield transport of intensity approach for high-throughput quantitative phase microscopy

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Advanced Photonics 2022年第5期4卷 15-26页

作者： Linpeng Lu Jiaji Li yefeng Shu Jiasong Sun Jie Zhou edmund y.lam Qian Chen Chao Zuo Nanjing University of Science and Technology Smart Computational Imaging Laboratory(SCILab)NanjingChina Nanjing University of Science and Technology School of Electronic and Optical EngineeringNanjingChina Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense NanjingChina Nanjing University of Science and Technology Smart Computational Imaging Research Institute(SCIRI)NanjingChina The University of Hong Kong Department of Electrical and Electronic EngineeringPokfulamHong KongChina

Transport of intensity equation(TIE)is a well-established non-interferometric phase retrieval approach that enables quantitative phase imaging(QPI)by simply measuring intensity images at multiple axially displaced *** advantage of a TIE-based QPI system is its compatibility with partially coherent illumination,which provides speckle-free imaging with resolution beyond the coherent diffraction ***,TIE is generally implemented with a brightfield(BF)configuration,and the maximum achievable imaging resolution is still limited to the incoherent diffraction limit(twice the coherent diffraction limit).It is desirable that TIE-related approaches can surpass this limit and achieve high-throughput[high-resolution and wide field of view(FOV)]*** propose a hybrid BF and darkfield transport of intensity(HBDTI)approach for highthroughput quantitative phase *** through-focus intensity stacks corresponding to BF and darkfield illuminations are acquired through a low-numerical-aperture(NA)objective *** high-resolution and large-FOV complex amplitude(both quantitative absorption and phase distributions)can then be synthesized based on an iterative phase retrieval algorithm taking the coherence model decomposition into *** effectiveness of the proposed method is experimentally verified by the retrieval of the USAF resolution target and different types of biological *** experimental results demonstrate that the half-width imaging resolution can be improved from 1230 nm to 488 nm with 2.5×expansion across a 4×FOV of 7.19 mm2,corresponding to a 6.25×increase in space-bandwidth product from∼5 to∼30.2 *** contrast to conventional TIE-based QPI methods where only BF illumination is used,the synthetic aperture process of HBDTI further incorporates darkfield illuminations to expand the accessible object frequency,thereby significantly extending the maximum available resolution from 2NA to∼5NA with a∼5×promotion of the coherent diffra

关键词： transport of intensity equation phase retrieval darkfield imaging high-throughput microscopy

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Effect of Lateritic Stone Aggregate and Coconut Husk Fiber on the Properties of Concrete

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Open Journal of Civil Engineering 2024年第2期14卷 240-257页

作者： edmund Borbi Humphrey Danso Emmanuel Appiah-Kubi Department of Construction Technology and Management Education Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Kumasi Ghana Department of Architecture and Civil Engineering Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development Kumasi Ghana

Natural stone aggregate forms the bulk volume of concrete and has contributed to the increased cost of concrete production. This has led to the search for alternate aggregates such as lateritic stone for concrete production. This paper investigates the engineering properties of concrete produced with lateritic aggregate (LA) as the coarse aggregate replacement and coconut husk fibre (CHF) as reinforcement. Natural stone aggregate was replaced by LA at 0%, 10%, 20%, 30%, 40%, and 50%, with 0.25% constant CHF by weight. A mix proportion of 1:1.5:3 with a water-cement ratio of 0.6 was used for producing concrete. A total of 162 specimens (90 cubes and 72 beams) were prepared and tested at the 7, 14, 21, and 28 days of curing. The highest compressive strength was 43.36 N/mm2 (10% LA replacement) as compared to the control of 41.51 N/mm2. The 10% LA replacement obtained the highest flexural strength of 5.35 N/mm2 as compared with the 5.29 N/mm2 for the control. The water absorption of the concrete increased from 2.8% (control) to 3.57% (50% replacement LA). Scanning electron microscopy (SEM) revealed micro gaps between CHF and LA concrete. The study, therefore, concludes that the use of LA and CHF positively influenced the strength properties of concrete. 10% LA replacement of coarse aggregate and 0.25% CHF is recommended to practitioners for use.

关键词： Concrete Coarse Aggregate Compressive Strength Flexural Strength Natural Stone Aggregate SEM

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An unsupervised learning approach to study synchroneity of past events in the South China Sea

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Frontiers of Earth Science 2019年第3期13卷 628-640页

作者： Kevin C. Tse Hon-Chim Chiu Man-yin Tsang yiliang Li edmund y. lam Department of Earth Sciences The University of Hong KongPokfulamHong KongChina Department of Geography and Centre for Geo-computation Studies Hong Kong Baptist UniversityKowloon TongHong KongChina Department of Earth Sciences University of TorontoCanada Department of Electrical and Electronic Engineering The University of Hong KongPokfulamHong KongChina

Unsupervised machine learning methods were applied on multivariate geophysical and geochemical datasets of ocean floor sediment cores collected from the South China Sea. The well-preserved and continuous core samples comprising high resolution Cenozoic sediment records enable scientists to carry out paleoenvironment studies in detail. Bayesian age-depth chronological models constructed from biostratigraphic control points for the drilling sites are applied on cluster boundaries generated from two popular unsupervised learning methods: K-means and random forest. The unsupervised learning methods experimented have produced compact and unambiguous clusters from the datasets, indicating that previously unknown data patterns can be revealed when all variables from the datasets are taken into account simultaneously. A study of synchroneity of past events represented by the cluster boundaries across geographically separated ocean drilling sites is achieved through converting the fixed depths of cluster boundaries into chronological ranges represented by Gaussian density plots which are then compared with known past events in the region. A Gaussian density peak at around 7.2 Ma has been identified from results of all three sites and it is suggested to coincide with the initiation of the East Asian monsoon. Contrary to traditional statistical approach, a priori assumptions are not required for unsupervised learning, and the clustering results serve as a novel data-driven proxy for studying the complex and dynamic processes of the paleoenvironment surrounding the ocean sediment. This work serves as a pioneering approach to extract valuable information of regional events and opens up a systematic and objective way to study the vast global ocean sediment datasets.

关键词： machine learning ocean sediments unsupervised classification

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