Compressed sensing (CS) provides an efficient way to acquire and reconstruct natural images from a reduced number of linear projection measurements at sub-Nyquist sampling rates....
Inspired by recent theoretical advances in compressive sensing (CS), we propose a new framework that combines the classical local discrete cosine transform used in image compressi...
Jiangtao Wen, Zhuoyuan Chen, Yuxing Han, John D. V...
This paper provides a mathematical analysis of transform compression in its relationship to linear and nonlinear approximation theory. Contrasting linear and nonlinear approximatio...
Albert Cohen, Ingrid Daubechies, Onur G. Guleryuz,...
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in co...
Abstract. Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Ny...