Capturing high-dimensional (HD) data is a long-term challenge in signal processing and
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …

This paper considers estimation of a random variable in Poisson noise with signal scaling
coefficient and dark current as explicit parameters of the noise model. Specifically, the paper …

Our temporally compressive imaging system reconstructs a high-speed image sequence
from a single, coded snapshot. The reconstruction quality, similar to that of other …

A new compressive sensing inversion framework is developed via exploiting the sparsity of
local overlap** patches, with the lensless compressive imaging as an exemplar …

It is well-established in the compressive sensing (CS) literature that sensing matrices whose
elements are drawn from independent random distributions exhibit enhanced reconstruction …

We consider the problem of finding a sparse solution for an underdetermined linear system
of equations when the known parameters on both sides of the system are subject to …

This work studies properties of the conditional mean estimator in vector Poisson noise. The
main emphasis is to study conditions on prior distributions that induce linearity of the …

Poisson count data is ubiquitously encountered in applications such as optical imaging,
social networks, and traffic monitoring, where the data is typically modeled after a Poisson …

We develop a lensless compressive imaging architecture, which consists of an aperture
assembly and a single sensor, without using any lens. An anytime algorithm is proposed to …