Читать книгу Graph Spectral Image Processing - Gene Cheung - Страница 2
Table of Contents
Оглавление1 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. Conclusion 9.5. References 10 Graph Neural Networks for Image Processing 10.1. Introduction 10.2. Supervised learning problems 10.3. Generative models for point clouds 10.4. Concluding remarks 10.5. References
8 Index
