Graph Spectral Image Processing

TL;DR

This paper reviews recent techniques in graph spectral image processing, where images are modeled as signals on graphs to enable advanced processing tasks like compression, restoration, filtering, and segmentation.

Contribution

It provides an overview of recent graph spectral methods applied to image and video processing, highlighting their applications and potential benefits.

Findings

Graph spectral methods improve image processing tasks.

Modeling images as graph signals enables new analysis tools.

Applications include compression, restoration, filtering, segmentation.

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 2D 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 article, 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.