Graph Spectral Image Processing

被引：161

作者：

Cheung, Gene ^{[1
]}

Magli, Enrico ^{[2
]}

Tanaka, Yuichi ^{[3
,4
]}

Ng, Michael K. ^{[5
]}

机构：

[1] Natl Inst Informat, Tokyo 1018430, Japan

[2] Politecn Torino, Dept Elect & Telecommun, Turin, Italy

[3] Tokyo Univ Agr & Technol, Grad Sch BASE, Koganei, Tokyo 1848588, Japan

[4] Japan Sci & Technol Agcy, PRESTO, Kawaguchi, Saitama 3320012, Japan

[5] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China

来源：

PROCEEDINGS OF THE IEEE | 2018年 / 106卷 / 05期

基金：

日本学术振兴会; 日本科学技术振兴机构;

关键词：

Graph signal processing; image processing; LEVEL SET METHOD; LAPLACIAN REGULARIZATION; ENERGY MINIMIZATION; FOURIER-TRANSFORM; WEIGHTED GRAPHS; ACTIVE CONTOURS; NONLOCAL IMAGE; NYSTROM METHOD; SPARSE; COMPRESSION;

D O I：

10.1109/JPROC.2018.2799702

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：907 / 930

页数：24

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