What is included with this book?
Sparse and Redundant Representations - Theoretical and Numerical Foundations | |
Prologue | p. 3 |
Underdetermined Linear Systems | p. 3 |
Regularization | p. 4 |
The Temptation of Convexity | p. 5 |
A Closer Look at l1 Minimization | p. 6 |
Conversion of (P1) to Linear Programming | p. 8 |
Promoting Sparse Solutions | p. 8 |
The l0-Norm and Implications | p. 12 |
The (P0) Problem - Our Main Interest | p. 13 |
The Signal Processing Perspective | p. 14 |
Further Reading | p. 14 |
Uniqueness and Uncertainty | p. 17 |
Treating the Two-Ortho Case | p. 17 |
An Uncertainty Principle | p. 18 |
Uncertainty of Redundant Solutions | p. 21 |
From Uncertainty to Uniqueness | p. 23 |
Uniqueness Analysis for the General Case | p. 23 |
Uniqueness via the Spark | p. 23 |
Uniqueness via the Mutual-Coherence | p. 25 |
Uniqueness via the Babel Function | p. 27 |
Upper-Bounding the Spark | p. 28 |
Constructing Grassmannian Matrices | p. 29 |
Summary | p. 30 |
Further Reading | p. 31 |
Pursuit Algorithms - Practice | p. 35 |
Greedy Algorithms | p. 35 |
The Core Idea | p. 35 |
The Orthogonal-Matching-Pursuit | p. 36 |
Other Greedy Methods | p. 39 |
Normalization | p. 41 |
Rate of Decay of the Residual in Greedy Methods | p. 43 |
Thresholding Algorithm | p. 45 |
Numerical Demonstration of Greedy Algorithms | p. 46 |
Convex Relaxation Techniques | p. 48 |
Relaxation of the l0-Norm | p. 48 |
Numerical Algorithms for Solving (P1) | p. 51 |
Numerical Demonstration of Relaxation Methods | p. 51 |
Summary | p. 52 |
Further Reading | p. 53 |
Pursuit Algorithms - Guarantees | p. 55 |
Back to the Two-Ortho Case | p. 55 |
OMP Performance Guarantee | p. 55 |
BP Performance Guarantee | p. 58 |
The General Case | p. 64 |
OMP Performance Guarantee | p. 65 |
Thresholding Performance Guarantee | p. 67 |
BP Performance Guarantee | p. 68 |
Performance of Pursuit Algorithms - Summary | p. 71 |
The Role of the Sign-Pattern | p. 71 |
Tropp's Exact Recovery Condition | p. 73 |
Summary | p. 76 |
Further Reading | p. 76 |
From Exact to Approximate Solutions | p. 79 |
General Motivation | p. 79 |
Stability of the Sparsest Solution | p. 80 |
Uniqueness versus Stability - Gaining Intuition | p. 80 |
Theoretical Study of the Stability of (P0¿) | p. 82 |
The RIP and Its Use for Stability Analysis | p. 86 |
Pursuit Algorithms | p. 89 |
OMP and BP Extensions | p. 89 |
Iteratively-Reweighed-Least-Squares (IRLS) | p. 91 |
The LARS Algorithm | p. 95 |
Quality of Approximations Obtained | p. 98 |
The Unitary Case | p. 101 |
Performance of Pursuit Algorithms | p. 103 |
BPDN Stability Guarantee | p. 103 |
Thresholding Stability Guarantee | p. 104 |
Summary | p. 107 |
Further Reading | p. 108 |
Iterative-Shrinkage Algorithms | p. 111 |
Background | p. 111 |
The Unitary Case - A Source of Inspiration | p. 112 |
Shrinkage For the Unitary case | p. 112 |
The BCR Algorithm and Variations | p. 113 |
Developing Iterative-Shrinkage Algorithms | p. 115 |
Surrogate Functions and the Prox Method | p. 115 |
EM and Bound-Optimization Approaches | p. 117 |
An IRLS-Based Shrinkage Algorithm | p. 119 |
The Parallel-Coordinate-Descent (PCD) Algorithm | p. 120 |
StOMP: A Variation on Greedy Methods | p. 123 |
Bottom Line - Iterative-Shrinkage Algorithms | p. 125 |
Acceleration Using Line-Search and SESOP | p. 127 |
Iterative-Shrinkage Algorithms: Tests | p. 127 |
Summary | p. 132 |
Further Reading | p. 134 |
Towards Average Performance Analysis | p. 137 |
Empirical Evidence Revisited | p. 137 |
A Glimpse into Probabilistic Analysis | p. 140 |
The Analysis Goals | p. 140 |
Two-Ortho Analysis by Candes & Romberg | p. 141 |
Probabilistic Uniqueness | p. 143 |
Donoho's Analysis | p. 143 |
Summary | p. 144 |
Average Performance of Thresholding | p. 144 |
Preliminaries | p. 144 |
The Analysis | p. 145 |
Discussion | p. 148 |
Summary | p. 150 |
Further Reading | p. 150 |
The Dantzig-Selector Algorithm | p. 153 |
Dantzig-Selector versus Basis-Pursuit | p. 153 |
The Unitary Case | p. 155 |
Revisiting the Restricted Isometry Machinery | p. 156 |
Dantzig-Selector Performance Guaranty | p. 157 |
Dantzig-Selector in Practice | p. 163 |
Summary | p. 164 |
Further Reading | p. 165 |
From Theory to Practice - Signal and Image Processing Applications | |
Sparsity-Seeking Methods in Signal Processing | p. 169 |
Priors and Transforms for Signals | p. 169 |
The Sparse-Land Model | p. 172 |
Geometric Interpretation of Sparse-Land | p. 173 |
Processing of Sparsely-Generated Signals | p. 176 |
Analysis Versus Synthesis Signal Modeling | p. 178 |
Summary | p. 180 |
Further Reading | p. 181 |
Image Deblurring - A Case Study | p. 185 |
Problem Formulation | p. 185 |
The Dictionary | p. 186 |
Numerical Considerations | p. 188 |
Experiment Details and Results | p. 191 |
Summary | p. 198 |
Further Reading | p. 199 |
MAP versus MMSE Estimation | p. 201 |
A Stochastic Model and Estimation Goals | p. 201 |
Background on MAP and MMSE | p. 202 |
The Oracle Estimation | p. 204 |
Developing the Oracle Estimator | p. 204 |
The Oracle Error | p. 206 |
The MAP Estimation | p. 208 |
Developing the MAP Estimator | p. 208 |
Approximating the MAP Estimator | p. 211 |
The MMSE Estimation | p. 212 |
Developing the MMSE Estimator | p. 212 |
Approximating the MMSE Estimator | p. 215 |
MMSE and MAP Errors | p. 218 |
More Experimental Results | p. 220 |
Summary | p. 224 |
Further Reading | p. 224 |
The Quest for a Dictionary | p. 227 |
Choosing versus Learning | p. 227 |
Dictionary-Learning Algorithms | p. 228 |
Core Questions in Dictionary-Learning | p. 229 |
The MOD Algorithm | p. 230 |
The K-SVD Algorithm | p. 231 |
Training Structured Dictionaries | p. 237 |
The Double-Sparsity Model | p. 239 |
Union of Unitary Bases | p. 241 |
The Signature Dictionary | p. 242 |
Summary | p. 244 |
Further Reading | p. 244 |
Image Compression - Facial Images | p. 247 |
Compression of Facial Images | p. 247 |
Previous Work | p. 249 |
Sparse-Representation-Based Coding Scheme | p. 250 |
The General Scheme | p. 251 |
VQ Versus Sparse Representations | p. 253 |
More Details and Results | p. 254 |
K-SVD Dictionaries | p. 255 |
Reconstructed Images | p. 255 |
Run-Time and Memory Usage | p. 260 |
Comparing to Other Techniques | p. 261 |
Dictionary Redundancy | p. 262 |
Post-Processing for Deblocking | p. 263 |
The Blockiness Artifacts | p. 263 |
Possible Approaches For Deblocking | p. 265 |
Learning-Based Deblocking Approach | p. 266 |
Deblocking Results | p. 267 |
Summary | p. 268 |
Further Reading | p. 269 |
Image Denoising | p. 273 |
General Introduction - Image Denoising | p. 273 |
The Beginning: Global Modeling | p. 274 |
The Core Image-Denoising Algorithm | p. 274 |
Various Improvements | p. 276 |
From Global to Local Modeling | p. 278 |
The General Methodology | p. 278 |
Learning the Shrinkage Curves | p. 279 |
Learned Dictionary and Globalizing the Prior | p. 286 |
The Non-Local-Means Algorithm | p. 292 |
3D-DCT Shrinkage: BM3D Denoising | p. 296 |
SURE for Automatic Parameter Setting | p. 297 |
Development of the SURE | p. 298 |
Demonstrating SURE to Global-Threhsolding | p. 300 |
Summary | p. 303 |
Further Reading | p. 303 |
Other Applications | p. 309 |
General | p. 309 |
Image Separation via MCA | p. 310 |
Image = Cartoon + Texture | p. 310 |
Global MCA for Image Separation | p. 312 |
Local MCA for Image Separation | p. 316 |
Image Inpainting and Impulsive Noise Removal | p. 324 |
Inpainting Sparse-Land Signals - Core Principles | p. 324 |
Inpainting Images - Local K-SVD | p. 327 |
Inpainting Images - The Global MCA | p. 335 |
Impulse-Noise Filtering | p. 338 |
Image Scale-Up | p. 341 |
Modeling the Problem | p. 343 |
The Super-Resolution Algorithm | p. 346 |
Scaling-Up Results | p. 349 |
Image Scale-Up: Summary | p. 351 |
Summary | p. 353 |
Further Reading | p. 354 |
Epilogue | p. 359 |
What is it All About? | p. 359 |
What is Still Missing? | p. 359 |
Bottom Line | p. 360 |
Notation | p. 363 |
Acronyms | p. 369 |
Index | p. 371 |
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