Publications in Scientific Journals:

P. Berger, G. Hannak, G. Matz:
"Graph Signal Recovery via Primal-Dual Algorithms for Total Variation Minimization";
IEEE Journal of Selected Topics in Signal Processing, 11 (2017).

English abstract:
We consider the problem of recovering a smooth graph signal from noisy samples taken on a subset of graph nodes. The smoothness of the graph signal is quantified in terms of total variation. We formulate the signal recovery task as a convex optimization problem that minimizes the total variation of the graph signal while controlling its global or node-wise empirical error. We propose a first-order primal-dual algorithm to solve these total variation minimization problems. A distributed implementation of the algorithm is devised to handle large-dimensional applications efficiently. We use synthetic and real-world data to extensively compare the performance of our approach with state-of-the-art methods.

graph theory;signal reconstruction;Graph signal recovery;primal-dual algorithms;total variation minimization;node-wise empirical error;first-order primal-dual algorithm;Noise measurement;Signal processing algorithms;Minimization;Computational modeling;Opt

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.