The emerging field of compressed sensing has potentially powerful implications for the
design of optical imaging devices. In particular, compressed sensing theory suggests that …

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by reducing the sampling rate required to acquire and stably recover …

We consider the problem of estimating a sparse signal from a set of quantized, Gaussian
noise corrupted measurements, where each measurement corresponds to an interval of …

A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …

Recent theoretical developments in the area of compressive sensing (CS) have the potential
to significantly extend the capabilities of digital data acquisition systems such as analog-to …

The recently emerged compressive sensing (CS) framework aims to acquire signals at
reduced sample rates compared to the classical Shannon-Nyquist rate. To date, the CS …

In compressive sensing (CS), the goal is to recover signals at reduced sample rate
compared to the classic Shannon-Nyquist rate. However, the classic CS theory assumes the …

In this paper, we study the problem of recovering sparse or compressible signals from
uniformly quantized measurements. We present a new class of convex optimization …

This paper presents Matched Sign Pursuit (MSP), a new greedy algorithm to perform sparse
signal reconstruction from signs of signal measurements, ie, measurements quantized to 1 …

We present an end-to-end image compression system based on compressive sensing. The
presented system integrates the conventional scheme of compressive sampling (on the …