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 …

Tasks across diverse application domains can be posed as large-scale optimization
problems, these include graphics, vision, machine learning, imaging, health, scheduling …

The evolution of computer-generated holography (CGH) algorithms has prompted significant
improvements in the performances of holographic displays. Nonetheless, they start to …

Optical imaging has traditionally relied on hardware to fulfill its imaging function, producing
output measures that mimic the original objects. Developed separately, digital algorithms …

Today's commodity camera systems rely on compound optics to map light originating from
the scene to positions on the sensor where it gets recorded as an image. To record images …

Hybrid refractive-diffractive lenses combine the light efficiency of refractive lenses with the
information encoding power of diffractive optical elements (DOE), showing great potential as …

Imaging through scattering media is a fundamental and pervasive challenge in fields
ranging from medical diagnostics to astronomy. A promising strategy to overcome this …

Diffractive blur and low light levels are two fundamental challenges in producing high-quality
photographs in under-display cameras (UDCs). In this paper, we incorporate phase masks …

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 …