no code implementations • 31 Oct 2022 • Selin Aviyente, Alejandro Frangi, Erik Meijering, Arrate Muñoz-Barrutia, Michael Liebling, Dimitri Van De Ville, Jean-Christophe Olivo-Marin, Jelena Kovačević, Michael Unser
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing.
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued.
In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and recovery of bandlimited graph signals that lie on them.
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.
GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs.
We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.
For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.
We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information.
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.