An Introduction to Compressive Sensing

标签：

rice大学压缩感知的书，想系统性学习CS的可以看看这本书

下载地址：http://cnx.org/exports/f70b6ba0-b9f0-460f-8828-e8fc6179e65f@5.12.pdf/an-introduction-to-compressive-sensing-5.12.pdf

1 Introduction
  1.1 Introduction to compressive sensing 
2 Sparse and Compressible Signal Models
  2.1 Introduction to vector spaces 
  2.2 Bases and frames  
  2.3 Sparse representations 
  2.4 Compressible signals  
3 Sensing Matrices
  3.1 Sensing matrix design  
  3.2 Null space conditions  
  3.3 The restricted isometry property  
  3.4 The RIP and the NSP  
  3.5 Matrices that satisfy the RIP 
  3.6 Coherence 
4 Sparse Signal Recovery via l1 Minimization
  4.1 Signal recovery via l1 minimization  
  4.2 Noise-free signal recovery 
  4.3 Signal recovery in noise  
  4.4 Instance-optimal guarantees revisited  
  4.5 The cross-polytope and phase transitions  

5 Algorithms for Sparse Recovery
  5.1 Sparse recovery algorithms 
  5.2 Convex optimization-based methods 
  5.3 Greedy algorithms  
  5.4 Combinatorial algorithms  
  5.5 Bayesian methods 
6 Applications of Compressive Sensing
  6.1 Linear regression and model selection 
  6.2 Sparse error correction 
  6.3 Group testing and data stream algorithms 
  6.4 Compressive medical imaging 
  6.5 Analog-to-information conversion 
  6.6 Single-pixel camera 
  6.7 Hyperspectral imaging 
  6.8 Compressive processing of manifold-modeled data 
  6.9 Inference using compressive measurements 
  6.10 Compressive sensor networks  
  6.11 Genomic sensing 
7 Appendices
  7.1 Sub-Gaussian random variables 
  7.2 Concentration of measure for sub-Gaussian random variables 
  7.3 Proof of the RIP for sub-Gaussian matrices 
  7.4 l1 minimization proof 

An Introduction to Compressive Sensing

标签：

原文地址：http://www.cnblogs.com/axlute/p/4523973.html