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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2021
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Table of Contents
Cover
Title Page
Copyright
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
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
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
List of Authors
Index
End User License Agreement
Guide
Cover
Table of Contents
Title Page
Copyright
Introduction to Graph Spectral Image Processing
Begin Reading
List of Authors
Index
End User License Agreement
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SCIENCES
Image, Field Director – Laure Blanc-Feraud
Compression, Coding and Protection of Images and Videos, Subject Head – Christine Guillemot
Graph Spectral Image Processing
Coordinated by
Gene Cheung
Enrico Magli
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2021
The rights of Gene Cheung and Enrico Magli to be identified as the author of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2021932054
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78945-028-6
ERC code:
PE7 Systems and Communication Engineering PE7_7 Signal processing
