LEADER 05843nam 2200721 450 001 9910828770503321 005 20230807204906.0 010 $a1-118-82698-1 010 $a1-118-82725-2 010 $a1-118-82707-4 035 $a(CKB)2670000000594059 035 $a(EBL)1895101 035 $a(SSID)ssj0001471844 035 $a(PQKBManifestationID)11825227 035 $a(PQKBTitleCode)TC0001471844 035 $a(PQKBWorkID)11433211 035 $a(PQKB)10508228 035 $a(MiAaPQ)EBC1895101 035 $a(Au-PeEL)EBL1895101 035 $a(CaPaEBR)ebr11017945 035 $a(OCoLC)902804366 035 $a(EXLCZ)992670000000594059 100 $a20150311h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aRegularization and Bayesian methods for inverse problems in signal and image processing /$fedited by Jean-Franc?ois Giovannelli, Je?ro?me Idier 210 1$aLondon, [England] ;$aHoboken, New Jersey :$cISTE Limited :$cHoboken, New Jersey,$d2015. 210 4$dİ2015 215 $a1 online resource (323 p.) 225 1 $aDigital Signal and Image Processing Series 300 $aDescription based upon print version of record. 311 $a1-84821-637-8 311 $a1-322-95012-1 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Introduction; I.1. Bibliography; 1: 3D Reconstruction in X-ray Tomography: Approach Example for Clinical Data Processing; 1.1. Introduction; 1.2. Problem statement; 1.2.1. Data formation models; 1.2.2. Estimators; 1.2.3. Algorithms; 1.3. Method; 1.3.1. Data formation models; 1.3.2. Estimator; 1.3.3. Minimization method; 1.3.3.1. Algorithm selection; 1.3.3.2. Minimization procedure; 1.3.4. Implementation of the reconstruction procedure; 1.4. Results; 1.4.1. Comparison of minimization algorithms; 1.4.2. Using a region of interest in reconstruction 327 $a1.4.3. Consideration of the polyenergetic character of the X-ray source1.4.3.1. Simulated data in 2D; 1.4.3.2. Real data in 3D; 1.5. Conclusion; 1.6. Acknowledgments; 1.7. Bibliography; 2: Analysis of Force-Volume Images in Atomic Force Microscopy Using Sparse Approximation; 2.1. Introduction; 2.2. Atomic force microscopy; 2.2.1. Biological cell characterization; 2.2.2. AFM modalities; 2.2.2.1. Isoforce and isodistance images; 2.2.2.2. Force spectroscopy; 2.2.2.3. Force-volume imaging; 2.2.3. Physical piecewise models; 2.2.3.1. Approach phase models; 2.2.3.2. Retraction phase models 327 $a2.3. Data processing in AFM spectroscopy2.3.1. Objectives and methodology in signal processing; 2.3.1.1. Detection of the regions of interest; 2.3.1.2. Parametric model fitting; 2.3.2. Segmentation of a force curve by sparse approximation; 2.3.2.1. Detecting jumps in a signal; 2.3.2.2. Joint detection of discontinuities at different orders; 2.3.2.3. Scalar and vector variable selection; 2.4. Sparse approximation algorithms; 2.4.1. Minimization of a mixed l2-l0 criterion; 2.4.2. Dedicated algorithms; 2.4.3. Joint detection of discontinuities; 2.4.3.1. Construction of the dictionary 327 $a2.4.3.2. Selection of scalar variables2.4.3.3. Selection of vector variables; 2.5. Real data processing; 2.5.1. Segmentation of a retraction curve: comparison of strategies; 2.5.2. Retraction curve processing; 2.5.3. Force-volume image processing in the approach phase; 2.6. Conclusion; 2.7. Bibliography; 3: Polarimetric Image Restoration by Non-local Means; 3.1. Introduction; 3.2. Light polarization and the Stokes-Mueller formalism; 3.3. Estimation of the Stokes vectors; 3.3.1. Estimation of the Stokes vector in a pixel; 3.3.1.1. Problem formulation 327 $a3.3.1.2. Properties of the constrained optimization problem3.3.1.3. Optimization algorithm; 3.3.2. Non-local means filtering; 3.3.3. Adaptive non-local means filtering; 3.3.3.1. The function ?; 3.3.3.2. Patches size and shape; 3.3.4. Application to the estimation of Stokes vectors; 3.4. Results; 3.4.1. Results with synthetic data; 3.4.1.1. Synthetic data and context evaluation presentation; 3.4.1.2. Results; 3.4.1.3. Significance of the proposed method for the estimation of the weights; 3.4.2. Results with real data; 3.5. Conclusion; 3.6. Bibliography 327 $a4: Video Processing and Regularized Inversion Methods 330 $aThe focus of this book is on "ill-posed inverseproblems". These problems cannot be solved only on the basisof observed data. The building of solutions involves therecognition of other pieces of a priori information. Thesesolutions are then specific to the pieces of information taken intoaccount. Clarifying and taking these pieces of information intoaccount is necessary for grasping the domain of validity and thefield of application for the solutions built. For too long,the interest in these problems has remained very limited in thesignal-image community. However, the community has since recog 410 0$aDigital signal and image processing series. 606 $aInverse problems (Differential equations) 606 $aBayesian statistical decision theory 606 $aSignal processing$xMathematics 606 $aImage processing$xMathematics 615 0$aInverse problems (Differential equations) 615 0$aBayesian statistical decision theory. 615 0$aSignal processing$xMathematics. 615 0$aImage processing$xMathematics. 676 $a515.35 702 $aGiovannelli$b Jean-Franc?ois 702 $aIdier$b Je?ro?me 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910828770503321 996 $aRegularization and Bayesian methods for inverse problems in signal and image processing$92147965 997 $aUNINA