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All-optical image denoising using a diffractive visual processor

Light(Science & Applications) 2024年第3期13卷 429-445页

作者： Çağatay Işıl Tianyi Gan Fazil Onuralp Ardic Koray Mentesoglu Jagrit Digani Huseyin Karaca Hanlong Chen Jingxi Li deniz mengu Mona Jarrahi Kaan Akşit Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA University College London Department of Computer ScienceLondonUnited Kingdom

Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input *** general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power ***,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of *** all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of *** results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz *** to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays.

关键词： remove rendering holographic

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All-optical synthesis of an arbitrary linear transformation using diffractive surfaces

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Light(Science & Applications) 2021年第10期10卷 1928-1948页

作者： Onur Kulce deniz mengu Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA

Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical ***,we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input(Ni)and output(No),where Ni and No represent the number of pixels at the input and output fields-of-view(FOVs),***,we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear *** addition to this data-free design approach,we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target *** compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven(deep learning-based)diffractive designs to all-optically perform(i)arbitrarily-chosen complex-valued transformations including unitary,nonunitary,and noninvertible transforms,(ii)2D discrete Fourier transformation,(iii)arbitrary 2D permutation operations,and(iv)high-pass filtered coherent *** analyses reveal that if the total number(N)of spatially-engineered diffractive features/neurons is≥Ni×No,both design methods succeed in all-optical implementation of the target transformation,achieving negligible ***,compared to data-free designs,deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N

关键词： arbitrary valued unitary

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All-optical information-processing capacity of diffractive surfaces

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Light(Science & Applications) 2021年第2期10卷 217-233页

The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and *** advances related to the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light-matter interactions and ***,we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output *** show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network,up to a limit that is dictated by the extent of the input and output *** diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference,learning,and generalization capabilities for different image classification tasks when compared with a single trainable diffractive *** analyses and conclusions are broadly applicable to various forms of diffractive surfaces,including,e.g.,plasmomc and/or dielectric-based metasurfaces and flat optics,which can be used to form all-optical processors.

关键词： surface. valued optics

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Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network

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Light(Science & Applications) 2022年第7期11卷 1423-1442页

作者： Jingxi Li Yi-Chun Hung Onur Kulce deniz mengu Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

Research on optical computing has recently attracted significant attention due to the transformative advances in machine *** different approaches,diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive,free-space optical ***,we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple,arbitrarily-selected linear transformations through a single diffractive network trained using deep *** this framework,an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic,and different target linear transformations(complex-valued)are uniquely assigned to different combinations of input/output polarization *** transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to diffferent input/output polarization *** results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons(N)approaches N_(p)N_(i)N_(o),where Ni and N_(o) represent the number of pixels at the input and output fields-of-view,respectively,and N_(p) refers to the number of unique linear transformations assigned to different input/output polarization *** polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.

关键词： valued arbitrarily Polarization

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Ensemble learning of diffractive optical networks

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Light(Science & Applications) 2021年第1期10卷 123-135页

作者： Md Sadman Sakib Rahman Jingxi Li deniz mengu Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine ***,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation *** deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and ***,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble *** independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification *** this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each *** results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.

关键词： networks collective successive

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Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

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Light(Advanced Manufacturing) 2023年第3期4卷 206-221页

作者： Yuhang Li Yi Luo deniz mengu Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical *** QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image ***,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase *** QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light *** all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and *** presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

关键词： Quantitative phase imaging Optical neural network Diffractive deep neural network Diffusive media All-optical computing

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Snapshot multispectral imaging using a diffractive optical network

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Light(Science & Applications) 2023年第5期12卷 789-808页

作者： deniz mengu Anika Tabassum Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA

Multispectral imaging has been used for numerous applications in e.g.,environmental monitoring,aerospace,defense,and ***,we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image *** diffractive multispectral imager performs spatially-coherent imaging over a large spectrum,and at the same time,routes a pre-determined set of spectral channels onto an array of pixels at the output plane,converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery ***,the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization *** numerical simulations,we present different diffractive network designs that achieve snapshot multispectral imaging with 4,9 and 16 unique spectral bands within the visible spectrum,based on passive spatially-structured diffractive surfaces,with a compact design that axially spans ~72λ_(m),where λ_(m) is the mean wavelength of the spectral band of ***,we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2×2=4 unique bands at terahertz *** to their compact form factor and computation-free,power-efficient and polarization-insensitive forward operation,diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

关键词： spectrum spectral spatially

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To image,or not to image:class-specific diffractive cameras with all-optical erasure of undesired objects

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eLight 2022年第1期2卷 165-184页

作者： Bijie Bai Yi Luo Tianyi Gan Jingtian Hu Yuhang Li Yifan Zhao deniz mengu Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos Angeles 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCAUSA

Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private *** methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital ***,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of *** diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input *** their fabrication,the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes,while instantaneously erasing objects of the other data classes at the output *** the same framework,we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras,where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption,while the other classes of objects are irreversibly erased from the output *** success of class-specific diffractive cameras was experimentally demonstrated using terahertz(THz)waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset,all-optically erasing the other handwritten *** diffractive camera design can be scaled to different parts of the electromagnetic spectrum,including,e.g.,the visible and infrared wavelengths,to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

关键词： optical image specific

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Class-specific differential detection in diffractive optical neural networks improves inference accuracy

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Advanced Photonics 2019年第4期1卷 2-14页

作者： Jingxi Li deniz mengu Yi Luo Yair Rivenson Aydogan Ozcan University of California at Los Angeles Department of Electrical and Computer EngineeringLos AngelesCaliforniaUnited States University of California at Los Angeles Department of BioengineeringLos AngelesCaliforniaUnited States University of California at Los Angeles California NanoSystems InstituteLos AngelesCaliforniaUnited States

Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine *** deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and *** demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light *** this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector *** this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,***,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in *** further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural *** this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep n

关键词： optical computation optical neural networks deep learning optical machine learning diffractive deep neural networks

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Design of task-specific optical systems using broadband diffractive neural networks

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Light(Science & Applications) 2019年第1期8卷 124-137页

作者： Yi Luo deniz mengu Nezih T.Yardimci Yair Rivenson Muhammed Veli Mona Jarrahi Aydogan Ozcan Electrical and Computer Engineering Department University of California420 Westwood PlazaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCA 90095USA

Deep learning has been transformative in many fields,motivating the emergence of various optical computing *** optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural ***-based all-optical object recognition systems,designed through this framework and fabricated by 3D printing,have been reported to recognize handwritten digits and fashion products,demonstrating all-optical inference and generalization to sub-classes of *** previous diffractive approaches employed monochromatic coherent light as the illumination ***,we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep *** experimentally validated the success of this broadband diffractive neural network architecture by designing,fabricating and testing seven different multi-layer,diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize(1)a series of tuneable,single-passband and dual-passband spectral filters and(2)spatially controlled wavelength *** the native or engineered dispersion of various material systems with a deep-learning-based design strategy,broadband diffractive neural networks help us engineer the light–matter interaction in 3D,diverging from intuitive and analytical design methods to create taskspecific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.

关键词： neural networks specific

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