In this paper, we present a unified analysis of RIP bounds for sparse signals recovery by
using ℓ p minimization with 0< p≤ 1 and provide optimal RIP bounds which can guarantee …

Recent advances in various fields such as telecommunications, biomedicine and
economics, among others, have created enormous amount of data that are often …

As an extension of the widely used ℓ r-minimization with 0< r≤ 1, a new non-convex
weighted ℓ r− ℓ1 minimization method is proposed for compressive sensing. The theoretical …

This paper discusses reconstruction of a signal from undersampled data in the situation that
the signal is sparse or approximately sparse in terms of a (possibly) highly overcomplete …

This article considers recovery of signals that are sparse or approximately sparse in terms of
a (possibly) highly overcomplete and coherent tight frame from undersampled data …

The weighted ℓ r− ℓ 1 minimization method with 0< r≤ 1 largely generalizes the classical ℓ r
minimization method and achieves very good performance in compressive sensing …

The paper discusses the relationship between the null space property (NSP) and the lq‐
minimization in compressed sensing. Several versions of the null space property, that is, the …

This paper provides a new theoretical support for block sparse recovery. By embedding prior
support information into the block ℓ p− ℓ 1 minimization with 0< p≤ 1, we establish a …

This paper discusses the reconstruction of signals from few measurements in the situation
that signals are sparse or approximately sparse in terms of a general frame via the l q …

In the paper, we introduce an unconstrained analysis model based on the ℓ 1− α ℓ 2 (0< α≤
1) minimization for the signal and image reconstruction. We develop some new technology …