Cover Image

Compressed Sensing for Engineers

Bibliographic Details
Main Author: Majumdar, Angshul
Corporate Author: Taylor & Francis
Format: eBook
Language:English
Published: Milton : Chapman and Hall/CRC, 2018.
Series:Devices, Circuits, and Systems Ser.
Subjects:
Compressed sensing (Telecommunication)
Image processing > Digital techniques.
Image compression.
Signal processing > Mathematics.
Acquisition comprimée.
Traitement d'images > Techniques numériques.
Compression d'images.
Traitement du signal > Mathématiques.
digital imaging.
Image compression
Image processing > Digital techniques
Signal processing > Mathematics
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover; Half Title; Title Page; Copyright Page; Table of Contents; Foreword; Preface; Acknowledgments; Author; 1: Introduction; Machine Learning; Signal Processing; Low-Rank Recovery; Signal Processing; Machine Learning; 2: Greedy Algorithms; Oracle Solution; (Im)Practical Solution; Intuitive Solution; Practical Solutions; Matching Pursuit; Orthogonal Matching Pursuit; Stagewise Orthogonal Matching; CoSamp; Stagewise Weak Orthogonal Matching; Gradient Pursuits; Suggested Reading; Appendix: MATLAB® Codes; 3: Sparse Recovery; Equivalence of l0-Norm and l1-Norm Minimization
  • On Number of EquationsFOCally Underdetermined System Solver; lp Minimization Using FOCally Underdetermined System Solver; Iterative Re-weighted Least Squares; Noisy (Practical) Scenario; Iterative Soft Thresholding Algorithm; Majorization Minimization; Solving the Unconstrained Problem; Split Bregman Technique; Solving l0-Norm Minimization; Smooth l0-Norm Minimization; Iterative Hard Thresholding Algorithm; lp-Norm Minimization-Noisy Scenario Iterative Re-weighted Least Squares; Modified Iterative Soft Thresholding Algorithm; Suggested Reading; Appendix: MATLAB® Codes; 4: Co-sparse Recovery
  • Majorization MinimizationSplit Bregman; Greedy Analysis Pursuit; Suggested Reading; Appendix: MATLAB® Codes; 5: Group Sparsity; Synthesis Prior; Solving the Equality-Constrained Problem; Solving the Unconstrained Problem; Smoothed l2,0-Minimization; Analysis Prior; Greedy Algorithms; Suggested Reading; Appendix: MATLAB® Codes; 6: Joint Sparsity; Convex Relaxation; Majorization Minimization; Solving the Synthesis Prior Problem; Solving the Analysis Prior Problem; Solving the Constrained Problem via Cooling; Greedy Methods; Appendix: MATLAB® Codes; 7: Low-Rank Matrix Recovery
  • Connections with Compressed SensingFOCUSS Algorithm; Singular Value Shrinkage; Singular Value Hard Thresholding; Split Bregman Technique; Matrix Factorization; Appendix: MATLAB® Codes; 8: Combined Sparse and Low-Rank Recovery; Compressive Principal Component Pursuit; Derivation of Principal Component Pursuit; Sparse and Low-Rank Model; Solving Synthesis Prior; Solving Analysis Prior; Suggested Reading; Appendix: MATLAB® Codes; 9: Dictionary Learning; Dictionary Learning; Transform Learning; Suggested Reading; Appendix: MATLAB® Codes; 10: Medical Imaging; X-Ray Computed Tomography
  • Compressed Sensing in Static Computed Tomographic ReconstructionExperimental Results; Compressed Sensing in Dynamic Computed Tomography; Magnetic Resonance Imaging Reconstruction; Single-Echo Static Magnetic Resonance Imaging Reconstruction; Multi-echo Magnetic Resonance Imaging; Physics of Magnetic Resonance Image Contrast; Group-Sparse Reconstruction of Multi-echo Magnetic Resonance Imaging; Group-Sparse Synthesis Prior; Group-Sparse Analysis Prior Formulation; Multi-coil Parallel Magnetic Resonance Imaging; Image Domain Methods; SENSitivity Encoding or SENSE; Regularized SENSE