The goal of compressed sensing is to estimate a vector from an underdetermined system of
noisy linear measurements, by making use of prior knowledge on the structure of vectors in …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

In recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …

Compressive sensing (CS) is a new approach to simultaneous sensing and compression of
sparse and compressible signals based on randomized dimensionality reduction. To …

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving
inverse problems with deep generative models. Instead of optimizing only over the initial …

We characterize the measurement complexity of compressed sensing of signals drawn from
a known prior distribution, even when the support of the prior is the entire space (rather than …

We compare and contrast from a geometric perspective a number of low-dimensional signal
models that support stable information-preserving dimensionality reduction. We consider …

The recently introduced compressive sensing (CS) framework enables digital signal
acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed …

We address the problem of super-resolution frequency recovery using prior knowledge of
the structure of a spectrally sparse, undersampled signal. In many applications of interest …