Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture
with greater angular resolution than is inherently possible from the physical dimensions of a …

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless
data acquired in the form of its diffraction patterns. These patterns are acquired through a …

Image reconstruction enhanced by regularizers, eg, to enforce sparsity, low rank or
smoothness priors on images, has many successful applications in vision tasks such as …

Low-light image enhancement is a crucial area of research in computer vision, aimed at
recovering normally exposed images from low-light images to facilitate high-level vision …

Signal restoration is an important constrained optimization problem with significant
applications in various domains. Although non-convex constrained optimization problems …

A power-balanced hybrid optical imaging system has a diffractive computational camera,
introduced in this paper, with image formation by a refractive lens and multilevel phase mask …

In this work, we propose a nonconvex stochastic alternating minimizing (SAM) method for
sparse phase retrieval, where an-sparse vector of length is recovered from phaseless linear …

Phase retrieval (PR) arises from the lack of phase information in the measures recorded by
optical sensors. Phase masks that modulate the optical field and reduce ambiguities in the …

Phase retrieval (PR) consists of recovering the phase information from captured intensity
measurements, known as coded diffraction patterns (CDPs). Non-convex algorithms for …

Low-light image enhancement is one of the challenges in robotics. For low-light images,
only adjusting the brightness of the image will inevitably amplify the noise and cause more …