{"product_id":"regularization-and-bayesian-methods-for-inverse-problems-in-signal-and-image-processing-hardback-9781848216372","title":"Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing (Hardback) 9781848216372","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eRegularization and Bayesian Methods for Inverse Problems in Signal and Image Processing\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJean-Francois Giovannelli (Edited by), JF Giovannelli (Author), Jérôme Idier (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781848216372, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 February 2015\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e322 pages\u003cbr\u003e24.1 x 16.3 x 2 cm, 0.907 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe 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. \u003cp\u003eFrom 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.\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eINTRODUCTION xi\u003cbr\u003e\u003ci\u003eJean-Francois GIOVANNELLI and Jerome IDIER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 1. 3D RECONSTRUCTION IN X-RAY TOMOGRAPHY: APPROACH EXAMPLE FOR CLINICAL DATA PROCESSING 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYves GOUSSARD\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. Problem statement 2\u003c\/p\u003e \u003cp\u003e1.3. Method 7\u003c\/p\u003e \u003cp\u003e1.4. Results 15\u003c\/p\u003e \u003cp\u003e1.5. Conclusion 26\u003c\/p\u003e \u003cp\u003e1.6. Acknowledgments 27\u003c\/p\u003e \u003cp\u003e1.7. Bibliography 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 2. ANALYSIS OF FORCE-VOLUME IMAGES IN ATOMIC FORCE MICROSCOPY USING SPARSE APPROXIMATION 31\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCharles SOUSSEN, David BRIE, Gregory FRANCIUS, Jerome IDIER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 31\u003c\/p\u003e \u003cp\u003e2.2. Atomic force microscopy 32\u003c\/p\u003e \u003cp\u003e2.3. Data processing in AFM spectroscopy 40\u003c\/p\u003e \u003cp\u003e2.4. Sparse approximation algorithms 43\u003c\/p\u003e \u003cp\u003e2.5. Real data processing 49\u003c\/p\u003e \u003cp\u003e2.6. Conclusion 52\u003c\/p\u003e \u003cp\u003e2.7. Bibliography 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3. POLARIMETRIC IMAGE RESTORATION BY NON-LOCAL MEANS 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSylvain FAISAN, Francois ROUSSEAU, Christian HEINRICH, Jihad ZALLAT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 57\u003c\/p\u003e \u003cp\u003e3.2. Light polarization and the Stokes–Mueller formalism 58\u003c\/p\u003e \u003cp\u003e3.3. Estimation of the Stokes vectors 61\u003c\/p\u003e \u003cp\u003e3.4. Results 72\u003c\/p\u003e \u003cp\u003e3.5. Conclusion 77\u003c\/p\u003e \u003cp\u003e3.6. Bibliography 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 4. VIDEO PROCESSING AND REGULARIZED INVERSION METHODS 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGuy LE BESNERAIS, Frederic CHAMPAGNAT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 81\u003c\/p\u003e \u003cp\u003e4.2. Three applications 82\u003c\/p\u003e \u003cp\u003e4.3. Dense image registration 88\u003c\/p\u003e \u003cp\u003e4.4. A few achievements based on direct formulation 92\u003c\/p\u003e \u003cp\u003e4.5. Conclusion 104\u003c\/p\u003e \u003cp\u003e4.6. Bibliography 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5. BAYESIAN APPROACH IN PERFORMANCE MODELING: APPLICATION TO SUPERRESOLUTION 109\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFrederic CHAMPAGNAT, Guy LE BESNERAIS, Caroline KULCSAR\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 109\u003c\/p\u003e \u003cp\u003e5.2. Performance modeling and Bayesian paradigm 111\u003c\/p\u003e \u003cp\u003e5.3. Superresolution techniques behavior 113\u003c\/p\u003e \u003cp\u003e5.4. Application examples 126\u003c\/p\u003e \u003cp\u003e5.5. Real data processing 130\u003c\/p\u003e \u003cp\u003e5.6. Conclusion 136\u003c\/p\u003e \u003cp\u003e5.7. Bibliography 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6. LINE SPECTRA ESTIMATION FOR IRREGULARLY SAMPLED SIGNALS IN ASTROPHYSICS 141\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSebastien BOURGUIGNON, Herve CARFANTAN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 141\u003c\/p\u003e \u003cp\u003e6.2. Periodogram, irregular sampling, maximum likelihood 144\u003c\/p\u003e \u003cp\u003e6.3. Line spectra models: spectral sparsity 146\u003c\/p\u003e \u003cp\u003e6.4. Prewhitening, CLEAN and greedy approaches 151\u003c\/p\u003e \u003cp\u003e6.5. Global approach and convex penalization 155\u003c\/p\u003e \u003cp\u003e6.6. Probabilistic approach for sparsity 159\u003c\/p\u003e \u003cp\u003e6.7. Conclusion 164\u003c\/p\u003e \u003cp\u003e6.8. Bibliography 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 7. JOINT DETECTION-ESTIMATION IN FUNCTIONAL MRI 169\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePhilippe CIUCIU, Florence FORBES, Thomas VINCENT, Lotfi CHAARI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction to functional neuroimaging 169\u003c\/p\u003e \u003cp\u003e7.2. Joint detection-estimation of brain activity 171\u003c\/p\u003e \u003cp\u003e7.3. Bayesian approach 178\u003c\/p\u003e \u003cp\u003e7.4. Scheme for stochastic MCMC inference 183\u003c\/p\u003e \u003cp\u003e7.5. Alternative variational inference scheme 184\u003c\/p\u003e \u003cp\u003e7.6. Comparison of both types of solutions 190\u003c\/p\u003e \u003cp\u003e7.7. Conclusion 194\u003c\/p\u003e \u003cp\u003e7.8. Bibliography 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8. MCMC AND VARIATIONAL APPROACHES FOR BAYESIAN INVERSION IN DIFFRACTION IMAGING 201\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHacheme AYASSO, Bernard DUCHENE, Ali MOHAMMAD-DJAFARI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 201\u003c\/p\u003e \u003cp\u003e8.2. Measurement configuration 204\u003c\/p\u003e \u003cp\u003e8.3. The forward model 206\u003c\/p\u003e \u003cp\u003e8.4. Bayesian inversion approach 211\u003c\/p\u003e \u003cp\u003e8.5. Results 220\u003c\/p\u003e \u003cp\u003e8.6. Conclusions 220\u003c\/p\u003e \u003cp\u003e8.7. Bibliography 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9. VARIATIONAL BAYESIAN APPROACH AND BI-MODEL FOR THE RECONSTRUCTION-SEPARATION OF ASTROPHYSICS COMPONENTS 225\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThomas RODET, Aurelia FRAYSSE, Hacheme AYASSO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 225\u003c\/p\u003e \u003cp\u003e9.2. Variational Bayesian methodology 228\u003c\/p\u003e \u003cp\u003e9.3. Exponentiated gradient for variational Bayesian 229\u003c\/p\u003e \u003cp\u003e9.4. Application: reconstruction-separation of astrophysical components 232\u003c\/p\u003e \u003cp\u003e9.5. Implementation of the variational Bayesian approach 236\u003c\/p\u003e \u003cp\u003e9.6. Results 240\u003c\/p\u003e \u003cp\u003e9.7. Conclusion 246\u003c\/p\u003e \u003cp\u003e9.8. Bibliography 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 10. KERNEL VARIATIONAL APPROACH FOR TARGET TRACKING IN A WIRELESS SENSOR NETWORK 251\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHichem SNOUSSI, Paul HONEINE, Cedric RICHARD\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 251\u003c\/p\u003e \u003cp\u003e10.2. State of the art: limitations of existing methods 252\u003c\/p\u003e \u003cp\u003e10.3. Model-less target tracking 254\u003c\/p\u003e \u003cp\u003e10.4. Simulation results 261\u003c\/p\u003e \u003cp\u003e10.5. Conclusion 264\u003c\/p\u003e \u003cp\u003e10.6. Bibliography 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 11. ENTROPIES AND ENTROPIC CRITERIA 267\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJean-Francois BERCHER\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction 267\u003c\/p\u003e \u003cp\u003e11.2. Some entropies in information theory 268\u003c\/p\u003e \u003cp\u003e11.3. Source coding with escort distributions and Renyi bounds 273\u003c\/p\u003e \u003cp\u003e11.4. A simple transition model 277\u003c\/p\u003e \u003cp\u003e11.5. Minimization of the Renyi divergence and associated entropies 281\u003c\/p\u003e \u003cp\u003e11.6. Bibliography 289\u003c\/p\u003e \u003cp\u003eLIST OF AUTHORS 293\u003c\/p\u003e \u003cp\u003eINDEX 297\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52449386496280,"sku":"9781848216372","price":111.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781848216372.jpg?v=1785197642","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/regularization-and-bayesian-methods-for-inverse-problems-in-signal-and-image-processing-hardback-9781848216372","provider":"Freshly Printed Books","version":"1.0","type":"link"}