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Nguyen, Xuan Vinh: Super-resolution compressed sensing for resolving time-of-flight multipath interferencesSuper-resolution Compressed Sensing für die Lösung von Time-of-Flight Mehrwegsinterferenz-Problem. 2017
Inhalt
Acknowledgments
Abstract
Zussamenfassung
List of publications
Contents
List of figures
List of tables
List of abbreviations
1 Introduction
1.1 Time-of-flight imaging camera and multipath interference problem
1.2 Multi-frequency TOF acquisition using CS
1.2.1 Super-resolution CS techniques
1.2.2 Motivation and key contribution
1.3 Outline
2 Time-of-flight principle
2.1 Pulsed modulation
2.2 Continuous wave modulation
2.2.1 Basic principles
2.2.2 TOF measurement accuracy and precision
2.3 Main hardware components
2.4 State-of-art TOF problems and solutions
2.4.1 Measurement linearity error
2.4.2 Photon shot noise
2.4.3 Saturation
2.4.4 Ambiguity range
2.4.5 Multipath interferences
2.5 Summary
3 Compressed sensing and TOF multipath problem
3.1 Compressed sensing (CS) and sparse recovery
3.1.1 l1 minimization or basis pursuit
3.1.2 Greedy pursuit
3.2 Sparse time profile of TOF multipath problem
3.2.1 Single modulation-frequency measurement
3.2.2 Discretization of time profile
3.3 Multiple frequency TOF (MFT) measurements
3.3.1 Mismatch model errors
3.3.2 Depth resolution of target discrimination
3.3.3 TOF modulation frequency limitation problem
3.4 Super-resolution MFT compressed sensing
3.4.1 Preliminary
3.4.2 High coherence of super-resolution sensing matrix
3.4.3 Minimum distance
3.4.4 Relaxed sparse support evaluation
3.4.5 Numerical analysis
3.5 Summary
4 Super-resolution compressed sensing methods
4.1 Band exclusion and local optimization
4.1.1 Band exclusion
4.1.2 Local optimization
4.1.3 BLOOMP
4.2 Modified cyclic orthogonal matching pursuit -OMP3
4.2.1 Global optimization
4.2.2 Modified cyclic OMP - OMP3
4.2.3 Combination between OMP3 and LO technique
4.3 Non-negative least squares optimization - POMP
4.3.1 Non-negative constraints
4.3.2 Negative atom removal module
4.3.3 Advantages and disadvantages
4.4 Non-negative magnitude adjustment orthogonal matching pursuit
4.4.1 Basic idea
4.4.2 Non-negative magnitude adjustment orthogonal matching pursuit - Ma-OMP
4.4.3 Modified non-negative magnitude adjustment orthogonal matching pursuit - Ma-OMP3
4.4.4 Optimized adjustment factor
4.5 Numerical results
4.5.1 Preliminaries
4.5.2 Optimized adjustment factor of Ma-OMP3
4.5.3 Comparison between different methods
4.5.4 Considered parameters
4.6 Combined OMP based on predicted minimum distance - CMD-OMP
4.6.1 Basic idea
4.6.2 Tuning parameter estimation
4.6.3 Minimum distance prediction
4.6.4 Numerical results
4.7 Summary
5 Multi-frequency selection optimization
5.1 Frequency selection optimization
5.1.1 Cyclic difference set
5.1.2 DFT sensing matrix
5.1.3 Proposed optimization method
5.2 Frequency and initial phase-offset optimization
5.3 Numerical results
5.3.1 CDS selection and the proposed frequency optimization method
5.3.2 Free parameter settings
5.4 Summary
6 Multiple measurement vector in super-resolution compressed sensing
6.1 Multipolarization TOF signal model
6.1.1 Polarized light
6.1.2 Multiple polarization
6.2 Multiple measurement vector (MMV) model
6.3 Conventional OMPMMV
6.3.1 Basic principle
6.3.2 Recovery guarantee
6.4 Modified variants of greedy pursuits for MMV model
6.4.1 Modified OMPMMV
6.4.2 Modified global optimization - GO-MMV
6.4.3 Cyclic orthogonal matching pursuit for MMV model - OMP3-MMV
6.4.4 Cyclic magnitude adjustment orthogonal matching pursuit for MMV model - Ma-OMP3-MMV
6.4.5 POMP for MMV model
6.5 Numerical results
6.5.1 Comparison between OMPMMV variants
6.5.2 Comparison between OMP3-MMV variants
6.5.3 Comparison between Ma-OMP3-MMV variants
6.5.4 Comparison between MMVs and SMV
6.5.5 Number of MMVs
6.6 Summary
7 Simultaneous multiple frequency acquisition
7.1 Projection matrix model
7.1.1 Dictionary matrix
7.1.2 MFT sensing matrix
7.1.3 Multiple-SMF sensing matrix
7.2 Projection matrix optimization in SMF acquisition
7.2.1 Projection matrix optimization method
7.2.2 Modified projection matrix modification
7.2.3 SMF model parameters
7.3 Numerical results
7.3.1 Coherence histogram
7.3.2 Support recovery performance
7.3.3 Multiple-SMF acquisition using MMV techniques
7.3.4 Relaxed super-resolution problem
7.3.5 Summary
7.4 Joint multiple frequency calibration
7.4.1 Numerical results
7.4.2 Summary
8 Experimental results
8.1 PMD Multicam System and measurement matrix formulation
8.1.1 PMD Multicam System
8.1.2 Dictionary matrix formulation
8.2 Transparent object imaging
8.3 Comparison of various reconstruction methods
8.3.1 POMP and OMP3
8.3.2 BPIC
8.3.3 Ma-OMP3
8.3.4 Supportive affects of LO techniques
8.3.5 Summary
8.4 CMD-OMP
8.5 Frequency and phase-offset selection optimization
8.6 SMF and joint multiple-frequency calibration
8.7 Summary
9 Conclusion
9.1 Super-resolution compressed sensing algorithms
9.2 Super-resolution MFT sensing matrix design
9.3 Relaxed super-resolution factor
A Appendix
A.1 Derivative of E(fi) w.r.t. fi
A.1.1 Computation of ui,q1,q2
A.1.2 Computation of vi,q1,q2
A.2 Derivative of E(i) w.r.t. i
Bibliography