Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Complex and inhomogeneous media are ubiquitous around us. Snow, fog, biological
tissues and turbid water—even just a piece of frosted glass—are opaque to light due to …

Imaging through scattering is an important yet challenging problem. Tremendous progress
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …

Computational imaging through scatter generally is accomplished by first characterizing the
scattering medium so that its forward operator is obtained and then imposing additional …

BACKGROUND Imaging technologies, which extend human vision capabilities, are such a
natural part of our current everyday experience that we often take them for granted …

Classification of an object behind a random and unknown scattering medium sets a
challenging task for computational imaging and machine vision fields. Recent deep learning …

In this paper, deep learning as a novel algorithm is proposed to reduce the noise of the
fringe patterns. Usually, the training samples are acquired through experimental acquisition …

Computational meta-imagers synergize metamaterial hardware with advanced signal
processing approaches such as compressed sensing. Recent advances in artificial …

The rapid proliferation of intelligent systems (eg, fully autonomous vehicles) in today's
society relies on sensors with low latency and computational effort. Yet current sensing …

Imaging through fog has important applications in industries such as self-driving cars,
augmented driving, airplanes, helicopters, drones and trains. Here we show that time …