frames for compressed sensing using coherence
we give some new results on sparse signal recovery in the presence of noise, forweighted spaces. traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satis ed. moreover, we give better estimationsthen the ones given recently by cai, wang and xu.
This paper establishes new sufficient conditions on the restricted isometry property (RIP) for compressed sensing with coherent tight frames. One of our main results shows that the RIP (adapted to D ) condition δk+θk,k<1 guarantees the stable recovery of all signals that are near
Our aim of this article is to reconstruct a signal from undersampled data in the situation that the signal is sparse in terms of a tight frame. We present a condition, which is independent of the coherence of the tight frame, to guarantee accurate recovery of signals which are sp
We give some new results on sparse signal recovery in the presence of noise, for weighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, for random dictionaries this condition is rarely satised. Moreover, we give better estimations then the ones
A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns wit
It has been shown that redundant signal representation, e.g. tight frame, plays important role in compressed sensing image restoration. In order to get a good sparse representation, one has made enduring efforts to pursue tight frames. Although there are some tight frames under w
Abstract The emergence of distributed video compressed sensing technology has effectively reduced the computational cost and power consumption of the video system. As the core content of the DCVS research, reconstruction algorithms are mostly focused on improving the reconstructi