Structural coloration generates colors by the interaction between incident light and micro-or
nano-scale structures. It has received tremendous interest for decades, due to advantages …

Nonlinear encoding of optical information can be achieved using various forms of data
representation. Here, we analyze the performances of different nonlinear information …

Complex field imaging, which captures both the amplitude and phase information of input
optical fields or objects, can offer rich structural insights into samples, such as their …

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 …

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront
distortions, with applications ranging from imaging to beam focusing. Here, we present a …

Optical neural networks (ONN) are experiencing a renaissance, driven by the transformative
impact of artificial intelligence, as arithmetic pressures are progressively increasing the …

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers
optimized using supervised deep learning to all-optically implement various computational …

Optical imaging and sensing systems based on diffractive elements have seen massive
advances over the last several decades. Earlier generations of diffractive optical processors …

As an optical processor, a diffractive deep neural network (D 2 NN) utilizes engineered
diffractive surfaces designed through machine learning to perform all-optical information …

Machine learning with artificial neural networks has recently transformed many scientific
fields by introducing new data analysis and information processing techniques. Despite …