| 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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