Regularization and Bayesian methods for inverse problems in signal and image processing /
The 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 intoac...
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| Other Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
London : Hoboken, NJ :
ISTE ; Wiley,
2015.
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| Series: | Digital signal and image processing series.
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| Subjects: | |
| Online Access: |
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Table of Contents:
- Cover; 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.
- 1.4.3. 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.
- 2.3. 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.
- 2.4.3.2. 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.