scholarly journals Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Yao Wang ◽  
Jianjun Wang
Keyword(s):  
Compressed Sensing ◽  
Sufficient Conditions ◽  
Tight Frame ◽  
Restricted Isometry Property ◽  
Tight Frames ◽  
Analysis Method

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 nearly k-sparse in terms of a coherent tight frame D via the l1-analysis method, which improves the existing ones in the literature.

THE RESTRICTED ISOMETRY PROPERTY FOR SIGNAL RECOVERY WITH COHERENT TIGHT FRAMES

2015 ◽  
Vol 92 (3) ◽  
pp. 496-507 ◽  
Author(s):  
FEN-GONG WU ◽  
DONG-HUI LI
Keyword(s):  
Compressed Sensing ◽  
Orthonormal Basis ◽  
Natural Extension ◽  
Tight Frame ◽  
Restricted Isometry Property ◽  
Tight Frames ◽  
Signal Recovery ◽  
Measurement Matrix

In this paper, we consider signal recovery via $l_{1}$-analysis optimisation. The signals we consider are not sparse in an orthonormal basis or incoherent dictionary, but sparse or nearly sparse in terms of some tight frame $D$. The analysis in this paper is based on the restricted isometry property adapted to a tight frame $D$ (abbreviated as $D$-RIP), which is a natural extension of the standard restricted isometry property. Assuming that the measurement matrix $A\in \mathbb{R}^{m\times n}$ satisfies $D$-RIP with constant ${\it\delta}_{tk}$ for integer $k$ and $t>1$, we show that the condition ${\it\delta}_{tk}<\sqrt{(t-1)/t}$ guarantees stable recovery of signals through $l_{1}$-analysis. This condition is sharp in the sense explained in the paper. The results improve those of Li and Lin [‘Compressed sensing with coherent tight frames via $l_{q}$-minimization for $0<q\leq 1$’, Preprint, 2011, arXiv:1105.3299] and Baker [‘A note on sparsification by frames’, Preprint, 2013, arXiv:1308.5249].

scholarly journals A Note on Block-Sparse Signal Recovery with Coherent Tight Frames

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Yao Wang ◽  
Jianjun Wang ◽  
Zongben Xu
Keyword(s):  
Sufficient Conditions ◽  
Tight Frame ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Sparse Signals ◽  
Tight Frames ◽  
Signal Recovery ◽  
Analysis Method ◽  
Measurement Matrix ◽  
Undersampled Data

This note discusses the recovery of signals from undersampled data in the situation that such signals are nearly block sparse in terms of an overcomplete and coherent tight frameD. By introducing the notion of blockD-restricted isometry property (D-RIP), we establish several sufficient conditions for the proposed mixedl2/l1-analysis method to guarantee stable recovery of nearly block-sparse signals in terms ofD. One of the main results of this note shows that if the measurement matrix satisfies the blockD-RIP with constantsδk<0.307, then the signals which are nearly blockk-sparse in terms ofDcan be stably recovered via mixedl2/l1-analysis in the presence of noise.

Stable recovery of sparse signals with coherent tight frames via lp-analysis approach

2016 ◽  
Vol 15 (04) ◽  
pp. 505-520
Author(s):  
Dekai Liu ◽  
Song Li
Keyword(s):  
Compressed Sensing ◽  
Minimization Problem ◽  
Tight Frame ◽  
Complex Case ◽  
Closed Form Expression ◽  
Sparse Signals ◽  
Tight Frames ◽  
Orthonormal Matrix ◽  
Redundant Dictionaries ◽  
True Signal

In this paper, we consider to recover a signal which is sparse in terms of a tight frame from undersampled measurements via [Formula: see text]-minimization problem for [Formula: see text]. In [Compressed sensing with coherent tight frames via [Formula: see text]-minimization for [Formula: see text], Inverse Probl. Imaging 8 (2014) 761–777], Li and Lin proved that when [Formula: see text] there exists a [Formula: see text], depending on [Formula: see text] such that for any [Formula: see text], each solution of the [Formula: see text]-minimization problem can approximate the true signal well. The constant [Formula: see text] is referred to as the [Formula: see text]-RIP constant of order [Formula: see text] which was first introduced by Candès et al. in [Compressed sensing with coherent and redundant dictionaries, Appl. Comput. Harmon. Anal. 31 (2011) 59–73]. The main aim of this paper is to give the closed-form expression of [Formula: see text]. We show that for every [Formula: see text]-RIP constant [Formula: see text], if [Formula: see text] where [Formula: see text] then the [Formula: see text]-minimization problem can reconstruct the true signal approximately well. Our main results also hold for the complex case, i.e. the measurement matrix, the tight frame and the signal are all in the complex domain. It should be noted that the[Formula: see text]-RIP condition is independent of the coherence of the tight frame (see [Compressed sensing with coherent and redundant dictionaries, Appl. Comput. Harmon. Anal. 31 (2011) 59–73]). In particular, when the tight frame reduces to an identity matrix or an orthonormal matrix, the conclusions in our paper coincide with the results appeared in [Stable recovery of sparse signals via [Formula: see text]-minimization, Appl. Comput. Harmon. Anal. 38 (2015) 161–176].

Compressed-Sensing MRI Based on Adaptive Tight Frame in Gradient Domain

2018 ◽  
Vol 49 (5) ◽  
pp. 465-477 ◽  
Author(s):  
Xiaoyu Fan ◽  
Qiusheng Lian ◽  
Baoshun Shi
Keyword(s):  
Compressed Sensing ◽  
Tight Frame ◽  
Gradient Domain

scholarly journals Generalized notions of sparsity and restricted isometry property. Part I: a unified framework

2019 ◽  
Vol 9 (1) ◽  
pp. 157-193 ◽  
Author(s):  
Marius Junge ◽  
Kiryung Lee
Keyword(s):  
Banach Space ◽  
Inverse Problems ◽  
Compressed Sensing ◽  
Dimensionality Reduction ◽  
Group Actions ◽  
Function Spaces ◽  
Restricted Isometry Property ◽  
Low Rank ◽  
Unified Framework ◽  
Order Tensor

Abstract The restricted isometry property (RIP) is an integral tool in the analysis of various inverse problems with sparsity models. Motivated by the applications of compressed sensing and dimensionality reduction of low-rank tensors, we propose generalized notions of sparsity and provide a unified framework for the corresponding RIP, in particular when combined with isotropic group actions. Our results extend an approach by Rudelson and Vershynin to a much broader context including commutative and non-commutative function spaces. Moreover, our Banach space notion of sparsity applies to affine group actions. The generalized approach in particular applies to high-order tensor products.

scholarly journals The Oscillation of a Class of the Fractional-Order Delay Differential Equations

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Qianli Lu ◽  
Feng Cen
Keyword(s):  
Differential Equations ◽  
Fractional Order ◽  
Delay Differential Equations ◽  
Sufficient Conditions ◽  
Necessary And Sufficient Conditions ◽  
Analysis Method ◽  
Constant Coefficients ◽  
Delay Differential ◽  
Necessary And Sufficient

Several oscillation results are proposed including necessary and sufficient conditions for the oscillation of fractional-order delay differential equations with constant coefficients, the sufficient or necessary and sufficient conditions for the oscillation of fractional-order delay differential equations by analysis method, and the sufficient or necessary and sufficient conditions for the oscillation of delay partial differential equation with three different boundary conditions. For this,α-exponential function which is a kind of functions that play the same role of the classical exponential functions of fractional-order derivatives is used.

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