  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.
