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Professor Dragotti

Signal Processing on Graphs

Traditional signal processing deals with signals which are defined on well-structured grids (e.g., Cartesian grids for images and videos or time-lines for time signals) and has been very successful in developing theories and methods to extract salient information from these well-ordered types of data. At the same time, data stored and shared in real-world complex systems (e.g., social networks, sensor networks, etc.) can be conveniently modelled as high-dimensional signals residing on the vertices of graphs, in which links are established based on connectivity and/or similarity. Graphical models have been extensively studied in the past, however, traditional approaches fail to encapsulate the many properties of the signals defined on graphs.

The goal of this project is to advance the theory of signal processing on graphs with the aim of merging graph theoretic concepts such as graph laplacian, graph clustering, node centrality with concepts in computational harmonic analysis such as compact or sparse signal representation, approximation and dimensionality reduction in order to devise new tools and algorithms for the intelligent management of large amounts of  data available over complex networks such as social networks. In particular, we aim to advance the theory of wavelets on graphs with particular emphasis on the extension of the notion of polynomial and exponential splines for signals defined on graphs.

Overview talk:

Main publications:

Research Areas

Wavelet theory.
Sampling theory.
Image and video processing and compression.
Image Based Rendering and Image Super-Resolution.
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