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Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging.
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.
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%.
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.
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.
