Graph Spectral Image Processing
1st Edition
Published on 6. August 2021
320 pages
978-1-119-85082-3 (ISBN)
Description
Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing - extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels - provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements.
The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.
The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.
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Gene Cheung | Enrico Magli
Graph Spectral Image Processing
Book
11/2021
1st Edition
ISTE Ltd
€164.50
Shipment within 3-4 weeks
Persons
Gene Cheung received his PhD in Electrical Engineering and Computer Science from the University of California, Berkeley, USA. He is Associate Professor at York University, Canada, and an IEEE fellow. His research interests include image and graph signal processing.
Enrico Magli is Full Professor at Politecnico di Torino, Italy, and is an IEEE fellow. His research interests are within the field of graph signal processing and deep learning for image and video analysis.
Enrico Magli is Full Professor at Politecnico di Torino, Italy, and is an IEEE fellow. His research interests are within the field of graph signal processing and deep learning for image and video analysis.
Content
- Cover
- Half-Title Page
- Title Page
- Copyright Page
- Contents
- 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
- Part 1. Fundamentals of Graph Signal Processing
- Chapter 1. Graph Spectral Filtering
- 1.1. Introduction
- 1.2. Review: filtering of time-domain signals
- 1.3. Filtering of graph signals
- 1.3.1. Vertex domain filtering
- 1.3.2. Spectral domain filtering
- 1.3.3. Relationship between graph spectral filtering and classical filtering
- 1.4. Edge-preserving smoothing of images as graph spectral filters
- 1.4. Edge-preserving smoothing of images as graph spectral filters
- 1.4.1. Early works
- 1.4.2. Edge-preserving smoothing
- 1.5. Multiple graph filters: graph filter banks
- 1.5.1. Framework
- 1.5.2. Perfect reconstruction condition
- 1.6. Fast computation
- 1.6.1. Subdivision
- 1.6.3. Precomputing GFT
- 1.6.4. Partial eigendecomposition
- 1.6.5. Polynomial approximation
- 1.6.6. Krylov subspace method
- 1.7. Conclusion
- 1.8. References
- Chapter 2. Graph Learning
- 2.1. Introduction
- 2.2. Literature review
- 2.2.1. Statistical models
- 2.2.2. Physically motivated models
- 2.3. Graph learning: a signal representation perspective
- 2.3.1. Models based on signal smoothness
- 2.3.2. Models based on spectral filtering of graph signals
- 2.3.3. Models based on causal dependencies on graphs
- 2.3.4. Connections with the broader literature
- 2.4. Applications of graph learning in image processing
- 2.5. Concluding remarks and future directions
- 2.6. References
- Chapter 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
- Part 2. Imaging Applications of Graph Signal Processing
- Chapter 4. Graph Spectral Image and Video Compression
- 4.1. Introduction
- 4.1.1. Basics of image and video compression
- 4.1.2. Literature review
- 4.1.3. Outline of the chapter
- 4.2. Graph-based models for image and video signals
- 4.2.1. Graph-based models for residuals of predicted signals
- 4.2.2. DCT/DSTs as GFTs and their relation to 1D models
- 4.2.3. Interpretation of graph weights for predictive transform coding
- 4.3. Graph spectral methods for compression
- 4.3.1. GL-GFT design
- 4.3.2. EA-GFT design
- 4.3.3. Empirical evaluation of GL-GFT and EA-GFT
- 4.4. Conclusion and potential future work
- 4.5. References
- Chapter 5. Graph Spectral 3D Image Compression
- 5.1. Introduction to 3D images
- 5.1.1. 3D image definition
- 5.1.2. Point clouds and meshes
- 5.1.3. Omnidirectional images
- 5.1.4. Light field images
- 5.1.5. Stereo/multi-view images
- 5.2. Graph-based 3D image coding: overview
- 5.3. Graph construction
- 5.3.1. Geometry-based approaches
- 5.3.2. Joint geometry and color-based approaches
- 5.3.3. Separable transforms
- 5.4. Concluding remarks
- 5.5. References
- Chapter 6. Graph Spectral Image Restoration
- 6.1. Introduction
- 6.1.1. A simple image degradation model
- 6.1.2. Restoration with signal priors
- 6.1.3. Restoration via filtering
- 6.1.4. GSP for image restoration
- 6.2. Discrete-domain methods
- 6.2.1. Non-local graph-based transform for depth image denoising
- 6.2.2. Doubly stochastic graph Laplacian
- 6.2.3. Reweighted graph total variation prior
- 6.2.4. Left eigenvectors of random walk graph Laplacian
- 6.2.5. Graph-based image filtering
- 6.3. Continuous-domain methods
- 6.3.1. Continuous-domain analysis of graph Laplacian regularization
- 6.3.2. Low-dimensional manifold model for image restoration
- 6.3.3. LDMM as graph Laplacian regularization
- 6.4. Learning-based methods
- 6.4.1. CNN with GLR
- 6.4.2. CNN with graph wavelet filter
- 6.5. Concluding remarks
- 6.6. References
- Chapter 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
- 7.3.1. Spectral-domain graph filtering for point clouds
- 7.3.2. Nodal-domain graph filtering for point clouds
- 7.3.3. Learning-based graph spectral methods for point clouds
- 7.4. Low-level point cloud processing
- 7.4.1. Point cloud denoising
- 7.4.2. Point cloud resampling
- 7.4.3. Datasets and evaluation metrics
- 7.5. High-level point cloud understanding
- 7.5.1. Data auto-encoding for point clouds
- 7.5.2. Transformation auto-encoding for point clouds
- 7.5.3. Applications of GraphTER in point clouds
- 7.5.4. Datasets and evaluation metrics
- 7.6. Summary and further reading
- 7.7. References
- Chapter 8. Graph Spectral Image Segmentation
- 8.1. Introduction
- 8.2. Pixel membership functions
- 8.2.1. Two-class problems
- 8.2.2. Multiple-class problems
- 8.2.3. Multiple images
- 8.3. Matrix properties
- 8.4. Graph cuts
- 8.4.1. The Mumford-Shah model
- 8.4.2. Graph cuts minimization
- 8.5. Summary
- 8.6. References
- Chapter 9. Graph Spectral Image Classification
- 9.1. Formulation of graph-based classification problems
- 9.1.1. Graph spectral classifiers with noiseless labels
- 9.1.2. Graph spectral classifiers with noisy labels
- 9.2. Toward practical graph classifier implementation
- 9.2.1. Graph construction
- 9.2.2. Experimental setup and analysis
- 9.3. Feature learning via deep neural network
- 9.3.1. Deep feature learning for graph construction
- 9.3.2. Iterative graph construction
- 9.3.3. Toward practical implementation of deep feature learning
- 9.3.4. Analysis on iterative graph construction for robust classification
- 9.3.5. Graph spectrum visualization
- 9.3.6. Classification error rate comparison using insufficient training data
- 9.3.7. Classification error rate comparison using sufficient training data with label noise
- 9.4. Conclusion
- 9.5. References
- Chapter 10. Graph Neural Networks for Image Processing
- 10.1. Introduction
- 10.2. Supervised learning problems
- 10.2.1. Point cloud classification
- 10.2.2. Point cloud segmentation
- 10.2.3. Image denoising
- 10.3. Generative models for point clouds
- 10.3.1. Point cloud generation
- 10.3.2. Shape completion
- 10.4. Concluding remarks
- 10.5. References
- List of Authors
- Index
- EULA
