320 pages - November 2021
ISBN papier : 9781789450286
ISBN ebook : 9781119850816


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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.

(FR) Part 1. Fundamentals of Graph Signal Processing
1. Graph Spectral Filtering, Yuichi Tanaka.
2. Graph Learning, Xiaowen Dong, Dorina Thanou, Michael Rabbat and Pascal Frossard.
3. Graph Neural Networks, Giulia Fracastoro and Diego Valsesia.

Part 2. Imaging Applications of Graph Signal Processing
4. Graph Spectral Image and Video Compression, Hilmi E. Egilmez, Yung-Hsuan Chao and Antonio Ortega.
5. Graph Spectral 3D Image Compression, Thomas Maugey, Mira Rizkallah, Navid Mahmoudian Bidgoli, Aline Roumy and Christine Guillemot.
6. Graph Spectral Image Restoration, Jiahao Pang and Jin Zeng.
7. Graph Spectral Point Cloud Processing, Wei Hu, Siheng Chen and Dong Tian.
8. Graph Spectral Image Segmentation, Michael Ng.
9. Graph Spectral Image Classification, Minxiang Ye, Vladimir Stankovic, Lina Stankovic and Gene Cheung.
10. Graph Neural Networks for Image Processing, Giulia Fracastoro and Diego Valsesia.

Gene Cheung

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

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.