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 …

The synthesis-based sparse representation model for signals has drawn considerable
interest in the past decade. Such a model assumes that the signal of interest can be …

Traditional sampling theories consider the problem of reconstructing an unknown signal x
from a series of samples. A prevalent assumption which often guarantees recovery from the …

We address the problem of reconstructing a multiband signal from its sub-Nyquist pointwise
samples, when the band locations are unknown. Our approach assumes an existing multi …

After a decade of extensive study of the sparse representation synthesis model, we can
safely say that this is a mature and stable field, with clear theoretical foundations, and …

Nonparametric Bayesian methods are considered for recovery of imagery based upon
compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is …

This paper investigates a new learning formulation called structured sparsity, which is a
natural extension of the standard sparsity concept in statistical learning and compressive …

摘要压缩感知(Compressive sensing, CS) 是一种全新的信息采集与处理的理论框架.
借助信号内在的稀疏性或可压缩性, 可从小规模的线性, 非自适应的测量中通过非线性优化的 …

Signals comprised of a stream of short pulses appear in many applications including
bioimaging and radar. The recent finite rate of innovation framework, has paved the way to …

This paper revisits the sparse multiple measurement vector (MMV) problem, where the aim
is to recover a set of jointly sparse multichannel vectors from incomplete measurements …