Hyperspectral image deconvolution

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Table of contents

1 Introduction 
1.1 Context and industrial objective
1.2 Hyperspectral imaging
1.2.1 Spectroscopy
1.2.2 Acquisition modes of a hyperspectral image
1.2.3 Toward industrial hyperspectral imaging
1.2.4 Hyperspectral microscopy
1.3 Super-resolution in hyperspectral images
1.3.1 Super-resolution in spectral microscopy
1.3.2 Super-resolution in industrial hyperspectral imaging
1.3.3 Observation model
1.4 Scientific contribution of the thesis
1.4.1 Regularization parameters estimation in non-negative hyperspectral image deconvolution
1.4.2 Online deconvolution for pushbroom imaging system
1.4.3 Joint unmixing and deconvolution of hyperspectral images
1.5 Organization of the thesis
1.6 Publications
2 Regularization parameter estimation for non-negative hyperspectral image deconvolution
2.1 Introduction
2.2 Hyperspectral image deconvolution
2.2.1 Discrete representation of the blurred images
2.2.2 Hyperspectral image deconvolution
2.3 Hyperspectral image deconvolution as a multi-objective optimization
2.3.1 Multi-objective Optimization
2.3.2 Shape of the estimated response surface
2.4 Choosing the Regularization Parameters
2.4.1 Maximum curvature criterion
2.4.2 Minimum distance criterion
2.4.3 A grid-search strategy for MDC
2.5 Examples and Experiments
2.5.1 Performances of MCC and MDC for 2D image deconvolution
2.5.2 An illustrative example of the non-negativity constrained hyperspectral image deconvolution
2.5.3 Performances of MCC and MDC for non-negative hyperspectral image deconvolution
2.5.4 Application to hyperspectral fluorescence microscopy
2.6 Conclusion
2.7 Supplementary material: behavior of the MDC and MCC for different types of hyperspectral images
2.7.1 Simulated hyperspectral images
2.7.2 Performance evaluation and result presentation
2.7.3 Discussion
3 Online deconvolution for industrial hyperspectral imaging systems 
3.1 Introduction
3.2 Blurring and causality issues
3.2.1 Scanning technologies and data structure
3.2.2 Blurring and noise
3.2.3 Causality
3.3 Online image deconvolution
3.3.1 Block Tikhonov
3.3.2 Sliding-block regularized LMS (SBR-LMS)
3.3.3 Algorithm implementation and computational cost
3.4 Transient behavior analysis
3.4.1 Mean and mean-squares transient behavior model
3.4.2 Stability condition
3.5 Experimental results
3.5.1 Validation of the transient behavior model
3.5.2 Effects of the parameters
3.5.3 Performances
3.5.4 Real hyperspectral image deblurring
3.6 Conclusions
4 Unmixing and deconvolution for hyperspectral images 
4.1 Introduction
4.2 Linear unmixing
4.2.1 Observation model
4.2.2 Joint unmixing-denoising (JUDN) method
4.2.3 Separated unmixing and denoising (SUDN) method
4.2.4 Comparison of JUDN and SUDN
4.3 Unmixing and deconvolution for hyperspectral images
4.3.1 Observation model
4.3.2 Offline unmixing and deconvolution
4.3.3 Online unmixing and deconvolution
4.3.4 Non-negative JUDC
4.3.5 Efficient implementation of the NN-JUDC
4.4 Experimental results
4.4.1 Simulated hyperspectral image
4.4.2 Application to wood waste sorting
4.5 Conclusion
5 Conclusion 
6 Résumé étendu 
6.1 Contexte et objectif industriel
6.2 Imagerie hyperspectrale
6.2.1 Spectroscopie
6.2.2 Acquisition d’une image hyperspectrale
6.2.3 Imagerie hyperspectrale industrielle
6.2.4 Microscopie spectrale
6.3 Superresolution in hyperspectral images
6.3.1 Super-resolution en microscopie spectrale
6.3.2 Super-résolution en imagerie hyperspectrale industrielle
6.3.3 Modèle d’observation
6.4 Contributions scientifiques de la thèse
6.5 Publications associées à la thèse
Bibliographie