Work

Graph Spectral Image Processing

Year: 2018

Type: article

Abstract: 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.

Source: Proceedings of the IEEE

DOI: 10.1109/jproc.2018.2799702

Authors: Gene Cheung, Enrico Magli, Yuichi Tanaka, Michael K. Ng

Institutions: National Institute of Informatics, Politecnico di Torino, Japan Science and Technology Agency, Tokyo University of Agriculture and Technology, Hong Kong Baptist University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green