The main obstacle for optical imaging or for sending information through turbid media such
as paint, clouds and biological tissue is the random scattering of light. Owing to its immense …

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential
for inverse design of photonic devices or nano-structures. Many of the recent works on …

Imaging can take many forms—from optical microscopes and telescopes through
ultrasonography to X-ray tomography. However, regardless of the imaging modality, the …

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 …

The last decade has seen the development of a wide set of tools, such as wavefront
sha**, computational or fundamental methods, that allow us to understand and control …

Optical imaging has had a central role in elucidating the underlying biological and
physiological mechanisms in living specimens owing to its high spatial resolution, molecular …

Deep learning has been proven to yield reliably generalizable solutions to numerous
classification and decision tasks. Here, we demonstrate for the first time to our knowledge …

Time-of-flight three-dimensional (3D) imaging has applications that range from industrial
inspection to motion tracking. Depth is recovered by measuring the round-trip flight time of …

The newly emerging field of wave front sha** in complex media has recently seen
enormous progress. The driving force behind these advances has been the experimental …

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