Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing

Jean-Francois Giovannelli, Jérôme Idier (Herausgeber)

Buch | Hardcover

322 Seiten

2015
ISTE Ltd and John Wiley & Sons Inc (Verlag)
978-1-84821-637-2 (ISBN)

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The focus of this book is on "ill-posed inverse problems". These problems cannot be solved only on the basis of observed data. The building of solutions involves the recognition of other pieces of a priori information. These solutions are then specific to the pieces of information taken into account. Clarifying and taking these pieces of information into account is necessary for grasping the domain of validity and the field of application for the solutions built. For too long, the interest in these problems has remained very limited in the signal-image community. However, the community has since recognized that these matters are more interesting and they have become the subject of much greater enthusiasm. From the application field’s point of view, a significant part of the book is devoted to conventional subjects in the field of inversion: biological and medical imaging, astronomy, non-destructive evaluation, processing of video sequences, target tracking, sensor networks and digital communications.

The variety of chapters is also clear, when we examine the acquisition modalities at stake: conventional modalities, such as tomography and NMR, visible or infrared optical imaging, or more recent modalities such as atomic force imaging and polarized light imaging.

Jean-François Giovannelli, Professor with Université de Bordeaux 1, France. Jérôme Idier is a researcher at IRCCyN (Institut de Recherches en Cybernetique de Nantes), France.

INTRODUCTION xi
Jean-Francois GIOVANNELLI and Jerome IDIER

CHAPTER 1. 3D RECONSTRUCTION IN X-RAY TOMOGRAPHY: APPROACH EXAMPLE FOR CLINICAL DATA PROCESSING 1
Yves GOUSSARD

1.1. Introduction 1

1.2. Problem statement 2

1.3. Method 7

1.4. Results 15

1.5. Conclusion 26

1.6. Acknowledgments 27

1.7. Bibliography 28

CHAPTER 2. ANALYSIS OF FORCE-VOLUME IMAGES IN ATOMIC FORCE MICROSCOPY USING SPARSE APPROXIMATION 31
Charles SOUSSEN, David BRIE, Gregory FRANCIUS, Jerome IDIER

2.1. Introduction 31

2.2. Atomic force microscopy 32

2.3. Data processing in AFM spectroscopy 40

2.4. Sparse approximation algorithms 43

2.5. Real data processing 49

2.6. Conclusion 52

2.7. Bibliography 53

CHAPTER 3. POLARIMETRIC IMAGE RESTORATION BY NON-LOCAL MEANS 57
Sylvain FAISAN, Francois ROUSSEAU, Christian HEINRICH, Jihad ZALLAT

3.1. Introduction 57

3.2. Light polarization and the Stokes–Mueller formalism 58

3.3. Estimation of the Stokes vectors 61

3.4. Results 72

3.5. Conclusion 77

3.6. Bibliography 78

CHAPTER 4. VIDEO PROCESSING AND REGULARIZED INVERSION METHODS 81
Guy LE BESNERAIS, Frederic CHAMPAGNAT

4.1. Introduction 81

4.2. Three applications 82

4.3. Dense image registration 88

4.4. A few achievements based on direct formulation 92

4.5. Conclusion 104

4.6. Bibliography 106

CHAPTER 5. BAYESIAN APPROACH IN PERFORMANCE MODELING: APPLICATION TO SUPERRESOLUTION 109
Frederic CHAMPAGNAT, Guy LE BESNERAIS, Caroline KULCSAR

5.1. Introduction 109

5.2. Performance modeling and Bayesian paradigm 111

5.3. Superresolution techniques behavior 113

5.4. Application examples 126

5.5. Real data processing 130

5.6. Conclusion 136

5.7. Bibliography 137

CHAPTER 6. LINE SPECTRA ESTIMATION FOR IRREGULARLY SAMPLED SIGNALS IN ASTROPHYSICS 141
Sebastien BOURGUIGNON, Herve CARFANTAN

6.1. Introduction 141

6.2. Periodogram, irregular sampling, maximum likelihood 144

6.3. Line spectra models: spectral sparsity 146

6.4. Prewhitening, CLEAN and greedy approaches 151

6.5. Global approach and convex penalization 155

6.6. Probabilistic approach for sparsity 159

6.7. Conclusion 164

6.8. Bibliography 165

CHAPTER 7. JOINT DETECTION-ESTIMATION IN FUNCTIONAL MRI 169
Philippe CIUCIU, Florence FORBES, Thomas VINCENT, Lotfi CHAARI

7.1. Introduction to functional neuroimaging 169

7.2. Joint detection-estimation of brain activity 171

7.3. Bayesian approach 178

7.4. Scheme for stochastic MCMC inference 183

7.5. Alternative variational inference scheme 184

7.6. Comparison of both types of solutions 190

7.7. Conclusion 194

7.8. Bibliography 195

CHAPTER 8. MCMC AND VARIATIONAL APPROACHES FOR BAYESIAN INVERSION IN DIFFRACTION IMAGING 201
Hacheme AYASSO, Bernard DUCHENE, Ali MOHAMMAD-DJAFARI

8.1. Introduction 201

8.2. Measurement configuration 204

8.3. The forward model 206

8.4. Bayesian inversion approach 211

8.5. Results 220

8.6. Conclusions 220

8.7. Bibliography 222

CHAPTER 9. VARIATIONAL BAYESIAN APPROACH AND BI-MODEL FOR THE RECONSTRUCTION-SEPARATION OF ASTROPHYSICS COMPONENTS 225
Thomas RODET, Aurelia FRAYSSE, Hacheme AYASSO

9.1. Introduction 225

9.2. Variational Bayesian methodology 228

9.3. Exponentiated gradient for variational Bayesian 229

9.4. Application: reconstruction-separation of astrophysical components 232

9.5. Implementation of the variational Bayesian approach 236

9.6. Results 240

9.7. Conclusion 246

9.8. Bibliography 246

CHAPTER 10. KERNEL VARIATIONAL APPROACH FOR TARGET TRACKING IN A WIRELESS SENSOR NETWORK 251
Hichem SNOUSSI, Paul HONEINE, Cedric RICHARD

10.1. Introduction 251

10.2. State of the art: limitations of existing methods 252

10.3. Model-less target tracking 254

10.4. Simulation results 261

10.5. Conclusion 264

10.6. Bibliography 264

CHAPTER 11. ENTROPIES AND ENTROPIC CRITERIA 267
Jean-Francois BERCHER

11.1. Introduction 267

11.2. Some entropies in information theory 268

11.3. Source coding with escort distributions and Renyi bounds 273

11.4. A simple transition model 277

11.5. Minimization of the Renyi divergence and associated entropies 281

11.6. Bibliography 289

LIST OF AUTHORS 293

INDEX 297

Verlagsort	London
Sprache	englisch
Maße	163 x 241 mm
Gewicht	907 g
Themenwelt	Mathematik / Informatik ► Informatik
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Nachrichtentechnik
ISBN-10	1-84821-637-8 / 1848216378
ISBN-13	978-1-84821-637-2 / 9781848216372
Zustand	Neuware