Graph Signal Recovery via Primal-Dual Algorithms for Total Variation Minimization


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.

DOI: 10.1109/JSTSP.2017.2726978

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@article{Berger2017GraphSR, title={Graph Signal Recovery via Primal-Dual Algorithms for Total Variation Minimization}, author={Peter Berger and Gabor Hannak and Gerald Matz}, journal={IEEE Journal of Selected Topics in Signal Processing}, year={2017}, volume={11}, pages={842-855} }