Richard G. Baraniuk

TL;DR: This lecture note presents a new method to capture and represent compressible signals at a rate significantly below the Nyquist rate, called compressive sensing, which employs nonadaptive linear projections that preserve the structure of the signal.

TL;DR: A new camera architecture based on a digital micromirror device with the new mathematical theory and algorithms of compressive sampling is presented that can operate efficiently across a broader spectral range than conventional silicon-based cameras.

TL;DR: In this article, the authors give a simple technique for verifying the restricted isometry property for random matrices that underlies compressive sensing, and obtain simple and direct proofs of Kashin's theorems on widths of finite balls in Euclidean space.

TL;DR: Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual- tree approach.

TL;DR: In this article, the authors introduce a new class of structured compressible signals along with a new sufficient condition for robust structured compressibility signal recovery that they dub the restricted amplification property, which is the natural counterpart to the restricted isometry property of conventional CS.