1  Cover

      2  Title Page

      3  Copyright

      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.