基于多层图谱的图像处理
Image Processing via Multilayer Graph Spectra
摘要：
图形信号处理（GSP）由于其揭示底层数据结构的能力，已成为图像处理的重要工具。然而，许多现实生活中的多媒体数据集，跨帧表现出异构的结构。多层图（MLG），而不是传统的单层图，提供更好的表示这些数据集，如视频和高光谱图像。为了将多层图模型推广到多层图模型，并为图像处理提供多层分析方法，本文提出了一种基于张量的多层图信号处理框架（M-GSP），并给出了适用于图像处理的M-GSP工具。然后，我们提出了M-GSP在图像处理中的应用指南，并介绍了几个应用，包括RGB图像压缩，边缘检测和高光谱图像分割。成功的实验结果证明了M-GSP在图像处理中的有效性和良好的应用前景。
英文摘要：
Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures across frames. Multilayer graphs (MLG), instead of traditional single-layer graphs, provide better representation of these datasets such as videos and hyperspectral images. To generalize GSP to multilayer graph models and develop multilayer analysis for image processing, this work introduces a tensor-based framework of multilayer graph signal processing (M-GSP) and present useful M-GSP tools for image processing. We then present guidelines for applying M-GSP in image processing and introduce several applications, including RGB image compression, edge detection and hyperspectral image segmentation. Successful experimental results demonstrate the efficacy and promising futures of M-GSP in image processing.
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