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Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.
Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.
All-optical complex field imaging using diffractive processors
Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing.
Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air.
Information hiding cameras: optical concealment of object information into ordinary images
This information hiding transformation is valid for infinitely many combinations of secret messages, all of which are transformed into ordinary-looking output patterns, achieved all-optically through passive light-matter interactions within the optical processor.
All-Optical Phase Conjugation Using Diffractive Wavefront Processing
Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing.
All-optical image denoising using a diffractive visual processor
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%.
Pyramid diffractive optical networks for unidirectional magnification and demagnification
Our analyses revealed the efficacy of this P-D2NN design in unidirectional image magnification and demagnification tasks, producing high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction - confirming the desired unidirectional imaging operation.
Learning Diffractive Optical Communication Around Arbitrary Opaque Occlusions
In this scheme, an electronic neural network encoder and a diffractive optical network decoder are jointly trained using deep learning to transfer the optical information or message of interest around the opaque occlusion of an arbitrary shape.
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs).
Snapshot Multispectral Imaging Using a Diffractive Optical Network
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 2x2=4 unique bands at terahertz spectrum.
Unidirectional Imaging using Deep Learning-Designed Materials
A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked.
Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
Permutation matrices form an important computational building block frequently used in various fields including e. g., communications, information security and data processing.
Super-resolution image display using diffractive decoders
We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects.
Terahertz Pulse Shaping Using Diffractive Surfaces
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.
Misalignment Resilient Diffractive Optical Networks
By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN).
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
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.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively.
