Search Results for author:
Found 8 papers, 0 papers with code
Unidirectional imaging with partially coherent light
Here, we report unidirectional imaging under spatially partially coherent light and demonstrate high-quality imaging only in the forward direction (A->B) with high power efficiency while distorting the image formation in the backward direction (B->A) along with low power efficiency.
Integration of Programmable Diffraction with Digital Neural Networks
Optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades.
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.
Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV).
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 Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is greater than or equal to ~2 Ni x No, where Ni and No refer to the number of useful pixels at the input and the output FOVs, respectively.
Time-lapse image classification using a diffractive neural network
Here we demonstrate, for the first time, a "time-lapse" image classification scheme using a diffractive network, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other.
Ensemble learning of diffractive optical networks
A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.
