Introduction to Compressive Sensing by Marco F. Duarte - HTML preview
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Introduction to Compressive Sensing
Table of Contents
- Chapter 1. Analog Sampling Theory
- Chapter 2. Sparsity and Compressibilty
- Chapter 3. Compressive Sensing
- 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
- 3.7. Sub-Gaussian random variables
- 3.8. Concentration of measure for sub-Gaussian random variables
- 3.9. Proof of the RIP for sub-Gaussian matrices
- Chapter 4. ℓ_1-norm minimization
- 4.1. Signal recovery via ℓ_1-norm 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
- 4.6. Sparse recovery algorithms
- 4.7. Convex optimization-based methods
- 4.8. Greedy algorithms
- 4.9. Combinatorial algorithms
- 4.10. Bayesian methods
- Chapter 5. Applications of Compressive Sensing
- 5.1. Linear regression and model selection
- 5.2. Sparse error correction
- 5.3. Group testing and data stream algorithms
- 5.4. Compressive medical imaging
- 5.5. Analog-to-information conversion
- 5.6. Single-pixel camera
- 5.7. Hyperspectral imaging
- 5.8. Compressive processing of manifold-modeled data
- 5.9. Inference using compressive measurements
- 5.10. Compressive sensor networks
- 5.11. Genomic sensing
- Index