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 …

Emerging single-photon-sensitive sensors produce picosecond-accurate time-stamped
photon counts. Applying advanced inverse methods to process these data has resulted in …

Multilayered artificial neural networks are becoming a pervasive tool in a host of application
fields. At the heart of this deep learning revolution are familiar concepts from applied and …

The problem of imaging through thick scattering media is encountered in many disciplines of
science, ranging from mesoscopic physics to astronomy. Photons become diffusive after …

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 …

Conventional sensor systems record information about directly visible objects, whereas
occluded scene components are considered lost in the measurement process. Non-line-of …

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 …

Objects obscured by occluders are considered lost in the images acquired by conventional
camera systems, prohibiting both visualization and understanding of such hidden objects …

Low signal-to-noise ratio (SNR) measurements, primarily due to the quartic attenuation of
intensity with distance, are arguably the fundamental barrier to real-time, high-resolution …

We present a neural modeling framework for non-line-of-sight (NLOS) imaging. Previous
solutions have sought to explicitly recover the 3D geometry (eg, as point clouds) or voxel …