Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D)
images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages …

We consider the total variation (TV) minimization problem used for compressive sensing and
solve it using the generalized alternating projection (GAP) algorithm. Extensive results …

In this work we present a deep learning framework for video compressive sensing. The
proposed formulation enables recovery of video frames in a few seconds at significantly …

In order to develop a fast and accurate algorithm for snapshot compressive sensing (SCI),
we combine the merits of two kinds of existing SCI methods: the interpretability of traditional …

Low-rank signal modeling has been widely leveraged to capture non-local correlation in
image processing applications. We propose a new method that employs low-rank tensor …

The Power consumption of servers in data centers depends greatly on the software running
on each server and how it interacts with the hardware. Different deployments of distributed …

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 overlapping patches, with the lensless compressive imaging as an exemplar …

We apply the Bayesian shrinkage dictionary learning into compressive dynamic-range
imaging. By attenuating the luminous intensity impinging upon the detector at the pixel level …

A compressive video microscope based on structured illumination is built. The source-side
illumination coding scheme allows the emission photons being collected by the full aperture …