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computational imaging without a computer:seeing through random diffusers at the speed of light

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eLight 2022年第1期2卷 42-57页

作者： Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesLos AngelesCA 90095USA.

imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using ***,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of *** deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase *** the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase *** experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during *** digital methods,all-optical diffractive reconstructions do not require power except for the illumination *** diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others.

关键词： imaging through diffusers computational imaging Diffractive neural network Deep learning

Artificial intelligence assisted light control and computational imaging through scattering media

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Journal of Innovative Optical Health Sciences 2019年第4期12卷 32-45页

作者： Shengfu Cheng Huanhao Li Yunqi Luo Yuanjin Zheng Puxiang Lai Department of Biomedical Engineering Hong Kong Polytechnic University Hong Kong SAR College of Material Science and Engineering Sichuan University SichuanP.R.China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around *** shaping is aimed at overcoming the strong scattering,featured by random interference,namely speckle *** randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber,yet this randomness is actually deterministic and potentially can be time reversal or *** wavefront shaping approaches,such as optical phase conjugation,iterative optimization,and transmission matrix measurement,have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering *** performance of these modula-tions,however,is far from *** recently,artifcial intelligence has brought new inspirations to this field,providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links *** this paper,we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning(deep learning in particular)for further advancements in the field.

关键词： Optical scattering deep learning wavefront shaping adaptive optics computational imaging

Handheld high-throughput plasmonic biosensor using computational on-chip imaging

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Light(Science & Applications) 2014年第1期3卷 388-397页

作者： Arif E Cetin Ahmet F Coskun Betty C Galarreta Min Huang David Herman Aydogan Ozcan Hatice Altug Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Departments of Electrical Engineering and Bioengineering University of CaliforniaLos Angeles(UCLA)Los AngelesCA 90095USA Division of Chemistry and Chemical Engineering California Institute of TechnologyPasadenaCA 91125USA Pontificia Universidad Catolica del Peru Departamento de Ciencias-QuimicaAvenida Universitaria 1801Lima 32Peru Bioengineering Department Ecole Polytechnique Federale de Lausanne(EPFL)Lausanne CH-1015Switzerland California NanoSystems Institute University of California Los AngelesLos Angeles(UCLA)Los AngelesCA90095USA

We demonstrate a handheld on-chip biosensing technology that employs plasmonic microarrays coupled with a lens-free computational imaging system towards multiplexed and high-throughput screening of biomolecular interactions for point-of-care applications and resource-limited *** lightweight and field-portable biosensing device,weighing 60 g and 7.5 cm tall,utilizes a compact optoelectronic sensor array to record the diffraction patterns of plasmonic nanostructures under uniform illumination by a single-light emitting diode tuned to the plasmonic mode of the *** a sensitive plasmonic array design that is combined with lens-free computational imaging,we demonstrate label-free and quantitative detection of biomolecules with a protein layer thickness down to 3 *** large-scale plasmonic microarrays,our on-chip imaging platform enables simultaneous detection of protein mono-and bilayers on the same platform over a wide range of biomolecule *** this handheld device,we also employ an iterative phase retrieval-based image reconstruction method,which offers the ability to digitally image a highly multiplexed array of sensors on the same plasmonic chip,making this approach especially suitable for high-throughput diagnostic applications in field settings.

关键词： computational imaging high-throughput biodetection lens-free imaging on-chip sensing plasmonics point of care diagnostics telemedicine

Enhancing the ability of single-pixel imaging against the source's energy fluctuation by complementary detection

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Chinese Optics Letters 2024年第3期22卷 19-24页

作者：蔡俊杰龚文林 School of Optoelectronic Science and Engineering Soochow UniversitySuzhou 215006China Key Laboratory of Modern Optical Technologies of the Ministry of Education Soochow UniversitySuzhou 215006China

The source's energy fluctuation has a great effect on the quality of single-pixel imaging(SPI).When the method of complementary detection is introduced into an SPI camera system and the echo signal is corrected with the summation of the light intensities recorded by two complementary detectors,we demonstrate,by both experiments and simulations,that complementary single-pixel imaging(CSPI)is robust to the source's energy *** superiority of the CSPI structure is also discussed in comparison with previous SPI via signal monitoring.

关键词： computational imaging image reconstruction complementary detection correlation function

Tensorial tomographic Fourier ptychography with applications to muscle tissue imaging

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Advanced Photonics 2024年第2期6卷 50-60页

作者： Shiqi Xu Xi Yang Paul Ritter Xiang Dai Kyung Chul Lee Lucas Kreiss Kevin C.Zhou Kanghyun Kim Amey Chaware Jadee Neff Carolyn Glass Seung Ah Lee Oliver Friedrich Roarke Horstmeyer Duke University DurhamNorth CarolinaUnited States Friedrich-Alexander University ErlangenGermany UC San Diego La JollaCaliforniaUnited States Yonsei University SeoulRepublic of Korea UC Berkeley BerkeleyCaliforniaUnited States Duke University Medical Center DurhamNorth CarolinaUnited States

We report tensorial tomographic Fourier ptychography(T^(2)oFu),a nonscanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in *** upon Fourier ptychography,a quantitative phase imaging technique,T^(2)oFu additionally highlights the vectorial nature of *** imaging setup consists of a standard microscope equipped with an LED matrix,a polarization generator,and a polarization-sensitive *** tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three *** demonstrate T^(2)oFu’s efficiency through volumetric reconstructions of refractive index,birefringence,and orientation for various validation samples,as well as tissue samples from muscle fibers and diseased heart *** reconstructions of healthy muscle fibers reveal their 3D fine-filament structures with consistent ***,we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.

关键词： computational imaging three-dimensional imaging phase retrieval microscopy polarization-sensitive imaging labelfree imaging

Deep learning-based scattering removal of light field imaging

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Chinese Optics Letters 2022年第4期20卷 16-22页

作者：王炜豪赵星姜智翔文雅 Institute of Modern Optics Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin 300350China

Light field imaging has shown significance in research fields for its high-temporal-resolution 3D imaging ***,in scenes of light field imaging through scattering,such as biological imaging in vivo and imaging in fog,the quality of 3D reconstruction will be severely reduced due to the scattering of the light field *** this paper,we propose a deep learning-based method of scattering removal of light field *** this method,a neural network,trained by simulation samples that are generated by light field imaging forward models with and without scattering,is utilized to remove the effect of scattering on light fields captured *** the deblurred light field and the scattering-free forward model,3D reconstruction with high resolution and high contrast can be *** demonstrate the proposed method by using it to realize high-quality 3D reconstruction through a single scattering layer experimentally.

关键词： computational imaging light field imaging scattering imaging deep learning

Resolution enhancement of digital holographic microscopy via synthetic aperture:a review

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Light(Advanced Manufacturing) 2022年第1期3卷 82-97页

作者： Peng Gao Caojin Yuan School of Physics and Optoelectronic Engineering Xidian UniversityXi’an 710071China Academy of Advanced Interdisciplinary research Xidian UniversityXi’an 710071China Key Laboratory for Opto-Electronic Technology of Jiangsu Province Nanjing Normal UniversityNanjing 210023China

Digital holographic microscopy(DHM),which combines digital holography with optical microscopy,is a wide field,minimally invasive quantitative phase microscopy(QPM)approach for measuring the 3D shape or the inner structure of transparent and translucent ***,limited by diffraction,the spatial resolution of conventional DHM is relatively low and incompatible with a wide field of view(FOV)owing to the spatial bandwidth product(SBP)limit of the imaging *** the past decades,many efforts have been made to enhance the spatial resolution of DHM while preserving a large FOV by trading with unused degrees of *** modulation techniques,such as oblique illumination,structured illumination,and speckle illumination,can enhance the resolution by adding more high-frequency information to the recording *** enhancement is also achieved by extrapolation of a hologram or by synthesizing a larger hologram by scanning the sample,the camera,or inserting a diffraction grating between the sample and the *** on-chip DHM,spatial resolution is achieved using pixel super-resolution *** this paper,we review various resolution enhancement approaches in DHM and discuss the advantages and disadvantages of these *** is our hope that this review will contribute to advancements in DHM and its practical applications in many fields.

关键词： Digital holographic microscopy Resolution enhancement computational imaging Illumination modulation

Compressed sensing sparse reconstruction for coherent field imaging

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Chinese Physics B 2016年第4期25卷 79-84页

作者：曹蓓罗秀娟张羽刘辉陈明徕 Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences

Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant in- fluence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future.

关键词： coherent field imaging computational imaging sparse reconstruction compressed sensing

Lensless complex amplitude demodulation based on deep learning in holographic data storage

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Opto-Electronic Advances 2023年第3期6卷 42-56页

作者： Jianying Hao Xiao Lin Yongkun Lin Mingyong Chen Ruixian Chen Guohai Situ Hideyoshi Horimai Xiaodi Tan College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China HolyMine Corporation 2032-2-301 OokaNumazuShizuoka 410-0022Japan

To increase the storage capacity in holographic data storage(HDS),the information to be stored is encoded into a complex *** and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in *** this study,we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level *** analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages,the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks(CNNs)to demodulate amplitude and phase *** experimental system is simple,stable,and robust,and it only needs a single diffraction image to realize the direct demodulation of both amplitude and *** our investigation,this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

关键词： holographic data storage complex amplitude demodulation deep learning computational imaging