TY - JOUR
T1 - Graph Spectral Image Processing
AU - Cheung, Gene
AU - Magli, Enrico
AU - Tanaka, Yuichi
AU - NG, Kwok Po
N1 - Funding Information:
Manuscript received September 15, 2017; revised December 12, 2017 and January 1, 2018; accepted January 17, 2018. Date of publication April 9, 2018; date of current version April 24, 2018. The work of Y. Tanaka was supported in part by the Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research (KAKENHI) under Grant JP16H04362; and in part by the Japan Science and Technology Agency (JST) Precursory Research for Embryonic Science and Technology (PRESTO) program under Grant JPMJPR1656. The work of M. K. Ng was supported by the Hong Kong Research Grants Council (HKRGC) General Research Fund (GRF) under Grants 12302715, 12306616, and 12200317; and by the Hong Kong Baptist University (HKBU) under Grant RC-ICRS/16-17/03. (Corresponding author: Gene Cheung.) G. Cheung is with the National Institute of Informatics, Tokyo 101-8430, Japan (e-mail: [email protected]). E. Magli is with the Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy (e-mail: [email protected]) Y. Tanaka is with the Graduate School of BASE, Tokyo University of Agriculture and Technology, Koganei, Tokyo, 184-8588 Japan and also with PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama 332-0012, Japan (e-mail: [email protected]). M. K. Ng is with the Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong (e-mail: [email protected]).
PY - 2018/5/1
Y1 - 2018/5/1
N2 - 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.
AB - 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.
KW - Graph signal processing
KW - image processing
UR - http://www.scopus.com/inward/record.url?scp=85045337027&partnerID=8YFLogxK
U2 - 10.1109/JPROC.2018.2799702
DO - 10.1109/JPROC.2018.2799702
M3 - Review article
AN - SCOPUS:85045337027
SN - 0018-9219
VL - 106
SP - 907
EP - 930
JO - Proceedings of the IEEE
JF - Proceedings of the IEEE
IS - 5
ER -