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 (≥ …

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

Compressive tomography consists of estimation of high-dimensional objects from lower
dimensional measurements. We review compressive tomography using radiation fields …

Compressive sensing of signals drawn from a Gaussian mixture model (GMM) admits closed-
form minimum mean squared error reconstruction from incomplete linear measurements. An …

A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from
temporally compressed video measurements. The GMM is used to model spatio-temporal …

This Letter presents a compressive camera that integrates mechanical translation and
spectral dispersion to compress a multi-spectral, high-speed scene onto a monochrome …

Focus and depth of field are conventionally addressed by adjusting longitudinal lens
position. More recently, combinations of deliberate blur and computational processing have …

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 …

We demonstrate a high-throughput computation-efficient snapshot coherence tomographic
imaging method by combining interferometric coding and compressive sampling. We first …

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 …