All-optical image denoising using a diffractive visual processor

Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input images.In 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 consumption.Here,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 light.This 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 interest.Our 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 spectrum.Owing 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.

Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning,our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, whereλ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.

High-throughput terahertz imaging:progress and challenges

Many exciting terahertz imaging applications,such as non-destructive evaluation,biomedical diagnosis,and security screening,have been historically limited in practical usage due to the raster-scanning requirement of imaging systems,which impose very low imaging speeds.However,recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications.Here,we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives.We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal,photon,and field image sensor arrays.We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight,spectroscopic,phase,and intensity image data at high throughputs.Furthermore,the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.

Snapshot multispectral imaging using a diffractive optical network

Multispectral imaging has been used for numerous applications in e.g.,environmental monitoring,aerospace,defense,and biomedicine.Here,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 field-of-view.This 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 algorithms.Furthermore,the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states.Through 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 interest.Moreover,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 spectrum.Due 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.

Gold-patched graphene nano-stripes for high-responsivity and ultrafast photodetection from the visible to infrared regime

Graphene is a very attractive material for broadband photodetection in hyperspectral imaging and sensing systems.However,its potential use has been hindered by tradeoffs between the responsivity,bandwidth,and operation speed of existing graphene photodetectors.Here,we present engineered photoconductive nanostructures based on goldpatched graphene nano-stripes,which enable simultaneous broadband and ultrafast photodetection with high responsivity.These nanostructures merge the advantages of broadband optical absorption,ultrafast photocarrier transport,and carrier multiplication within graphene nano-stripes with the ultrafast transport of photocarriers to gold patches before recombination.Through this approach,high-responsivity operation is realized without the use of bandwidth-limiting and speed-limiting quantum dots,defect states,or tunneling barriers.We demonstrate highresponsivity photodetection from the visible to infrared regime(0.6 A/W at 0.8μm and 11.5 A/W at 20μm),with operation speeds exceeding 50 GHz.Our results demonstrate improvement of the response times by more than seven orders of magnitude and an increase in bandwidths of one order of magnitude compared to those of higherresponsivity graphene photodetectors based on quantum dots and tunneling barriers.

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

Deep learning has been transformative in many fields,motivating the emergence of various optical computing architectures.Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks.Diffraction-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 data.These previous diffractive approaches employed monochromatic coherent light as the illumination source.Here,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 learning.We 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 de-multiplexing.Merging 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.

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

Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using 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 diffusers.After 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 diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This 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.

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

Privacy protection is a growing concern in the digital era,with machine vision techniques widely used throughout public and private settings.Existing methods address this growing problem by,e.g.,encrypting camera images or obscuring/blurring the imaged information through digital algorithms.Here,we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view.After 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 field-of-view.Using 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 image.The 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 digits.This 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.