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- Publisher Website: 10.1109/JPROC.2018.2799702
- Scopus: eid_2-s2.0-85045337027
- WOS: WOS:000433349100009
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Article: Graph Spectral Image Processing
Title | Graph Spectral Image Processing |
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Authors | |
Keywords | image processing Graph signal processing |
Issue Date | 2018 |
Citation | Proceedings of the IEEE, 2018, v. 106, n. 5, p. 907-930 How to Cite? |
Abstract | © 1963-2012 IEEE. 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. |
Persistent Identifier | http://hdl.handle.net/10722/276518 |
ISSN | 2023 Impact Factor: 23.2 2023 SCImago Journal Rankings: 6.085 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Cheung, Gene | - |
dc.contributor.author | Magli, Enrico | - |
dc.contributor.author | Tanaka, Yuichi | - |
dc.contributor.author | Ng, Michael K. | - |
dc.date.accessioned | 2019-09-18T08:33:51Z | - |
dc.date.available | 2019-09-18T08:33:51Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the IEEE, 2018, v. 106, n. 5, p. 907-930 | - |
dc.identifier.issn | 0018-9219 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276518 | - |
dc.description.abstract | © 1963-2012 IEEE. 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE | - |
dc.subject | image processing | - |
dc.subject | Graph signal processing | - |
dc.title | Graph Spectral Image Processing | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JPROC.2018.2799702 | - |
dc.identifier.scopus | eid_2-s2.0-85045337027 | - |
dc.identifier.volume | 106 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 907 | - |
dc.identifier.epage | 930 | - |
dc.identifier.isi | WOS:000433349100009 | - |
dc.identifier.issnl | 0018-9219 | - |