Graph-signal reconstruction and blind deconvolution for diffused sparse inputs
David Ramírez, Antonio G. Marques, Santiago Segarra
Abstract
This paper investigates the problems of signal reconstruction and blind deconvolution for graph signals that have been generated by an originally sparse input diffused through the network via the application of a graph filter operator. Assuming that the support of the sparse input signal is unknown, and that the diffused signal is observed only at a subset of nodes, we address the related problems of: 1) identifying the input and 2) interpolating the values of the diffused signal at the non-sampled nodes. We first consider the more tractable case where the coefficients of the diffusing graph filter are known and then address the problem of joint input and filter identification. The corresponding blind identification problems are formulated, novel convex relaxations are discussed, and modifications to incorporate a priori information on the sparse inputs are provided.
BibTeX
@inproceedings{icassp2017_graphsignalrecon,
title = {Graph-signal reconstruction and blind deconvolution for diffused sparse inputs},
author = {David Ramírez and Antonio G. Marques and Santiago Segarra},
booktitle = {ICASSP 2017},
year = {2017}
}