The goal of spectral imaging is to capture the spectral signature of a target. Traditional
scanning method for spectral imaging suffers from large system volume and low image …

Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …

Optical imaging is commonly used for both scientific and technological applications across
industry and academia. In image sensing, a measurement, such as of an object's position or …

Cameras are designed with a complicated tradeoff between image quality (eg sharpness,
contrast, color fidelity), and practical considerations such as cost, form factor, and weight …

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 …

The race for miniature color cameras using flat meta-optics has rapidly developed the end-to-
end design framework using neural networks. Although a large body of work has shown the …

Monocular depth estimation remains a challenging problem, despite significant advances in
neural network architectures that leverage pictorial depth cues alone. Inspired by depth from …

Deep optical optimization has recently emerged as a new paradigm for designing
computational imaging systems using only the output image as the objective. However, it …

While conventional cameras offer versatility for applications ranging from amateur
photography to autonomous driving, computational cameras allow for domain-specific …

Diffractive snapshot hyperspectral imaging based on the deep optics framework has been
striving to capture the spectral images of dynamic scenes. However, existing deep optics …