BibTex format
@inproceedings{Leung:2019,
author = {Leung, VCH and Huang, J-J and Dragotti, PL},
title = {Reconstruction of FRI Signals using Deep Neural Networks},
url = {http://arxiv.org/abs/1905.11935v1},
year = {2019}
}
Publications
Citation
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited signals, such as streams of Diracs. Widely used FRI reconstruction methods including Prony's method and matrix pencil method involve Singular Value Decomposition (SVD). When samples are corrupted with noise, they achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain Signal-to-Noise Ratio (SNR) due to the so-called subspace swap problem. In this paper, we investigate a deep neural network approach for FRI signal reconstruction that directly learns a transformation from signal samples to FRI parameters. Simulations show significant improvement on the breakdown SNR over existing FRI methods.
AU - Leung,VCH
AU - Huang,J-J
AU - Dragotti,PL
PY - 2019///
TI - Reconstruction of FRI Signals using Deep Neural Networks
UR - http://arxiv.org/abs/1905.11935v1
ER -