Graph Spectral Image Processing

• Published in: Proceedings of the IEEE Vol. 106; no. 5; pp. 907 - 930
• Main Authors: Cheung, Gene, Magli, Enrico, Tanaka, Yuichi, Ng, Michael K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-05-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Digital images; Digital imaging; Eigenvalues and eigenfunctions; Filtration; Graph signal processing; Graph theory; Image coding; Image compression; Image edge detection; Image filters; Image processing; Image restoration; Image segmentation; Laplace equations; Pixels; Remote sensors; Signal processing; Social network services; Spectra; Spectral analysis; Transforms; Video compression; Wireless sensor networks
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/JPROC.2018.2799702

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this paper, we overview recent graph spectral techniques in GSP specifically for image/video processing. The topics covered include image compression, image restoration, image filtering, and image segmentation.