Graph Spectral Image Processing
Graph Spectral Image Processing
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DOI：
10.1109/jproc.2018.2799702
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发表时间：
2018-05-01
影响因子：
20.6
通讯作者：
Ng, Michael K.
中科院分区：
文献类型：
--
作者：
Cheung, Gene;Magli, Enrico;Ng, Michael K.
图形信号处理（GSP）的最近出现已经刺激了对自然地存在于由图形描述的不规则数据核上的信号的深入研究（例如，社交网络、无线传感器网络）。虽然数字图像包含驻留在规则采样的2-D网格上的像素，但是如果可以设计将像素与反映图像结构的权重连接的适当的底层图，则可以将图像（或图像块）解释为图上的信号，并且应用GSP工具来处理和分析图频谱域中的信号。在本文中，我们概述了最近的图形频谱技术在GSP专门用于图像/视频处理。涵盖的主题包括图像压缩，图像恢复，图像滤波和图像分割。
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.