Table of Contents
1 Cover
4 Introduction to Graph Spectral Image Processing I.1. Introduction I.2. Graph definition I.3. Graph spectrum I.4. Graph variation operators I.5. Graph signal smoothness priors I.6. References
5 PART 1 Fundamentals of Graph Signal Processing 1 Graph Spectral Filtering 1.1. Introduction 1.2. Review: filtering of time-domain signals 1.3. Filtering of graph signals 1.4. Edge-preserving smoothing of images as graph spectral filters 1.5. Multiple graph filters: graph filter banks 1.6. Fast computation 1.7. Conclusion 1.8. References 2 Graph Learning 2.1. Introduction 2.2. Literature review 2.3. Graph learning: a signal representation perspective 2.4. Applications of graph learning in image processing 2.5. Concluding remarks and future directions 2.6. References 3 Graph Neural Networks 3.1. Introduction 3.2. Spectral graph-convolutional layers 3.3. Spatial graph-convolutional layers 3.4. Concluding remarks 3.5. References
6
PART 2 Imaging Applications of Graph Signal Processing
4 Graph Spectral Image and Video Compression
4.1. Introduction
4.2. Graph-based models for image and video signals
4.3. Graph spectral methods for compression
4.4. Conclusion and potential future work
4.5. References
5 Graph Spectral 3D Image Compression
5.1. Introduction to 3D images
5.2. Graph-based 3D image coding: overview
5.3. Graph construction
5.4. Concluding remarks
5.5. References
6 Graph Spectral Image Restoration
6.1. Introduction
6.2. Discrete-domain methods
6.3. Continuous-domain methods
6.4. Learning-based methods
6.5. Concluding remarks
6.6. References
7 Graph Spectral Point Cloud Processing
7.1. Introduction
7.2. Graph and graph-signals in point cloud processing
7.3. Graph spectral methodologies for point cloud processing
7.4. Low-level point cloud processing
7.5. High-level point cloud understanding
7.6. Summary and further reading
7.7. References
8 Graph Spectral Image Segmentation
8.1. Introduction
8.2. Pixel membership functions
8.3. Matrix properties
8.4. Graph cuts
8.5. Summary
8.6. References
9 Graph Spectral Image Classification
9.1. Formulation of graph-based classification problems
9.2. Toward practical graph classifier implementation
9.3. Feature learning via deep neural network
9.4.