Super-resolution of positive spikes by Toeplitz low-rank approximation. Condat, L. & Hirabayashi, A. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 459-463, Aug, 2015.
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Paper doi abstract bibtex
Paper doi abstract bibtex Super-resolution consists in recovering the fine details of a signal from low-resolution measurements. Here we con sider the estimation of Dirac pulses with positive amplitudes at arbitrary locations, from noisy lowpass-filtered samples. Maximum-likelihood estimation of the unknown parameters amounts to a difficult nonconvex matrix problem of structured low rank approximation. To solve it, we propose a new heuristic iterative algorithm, yielding state-of-the-art results.
@InProceedings{7362425,
author = {L. Condat and A. Hirabayashi},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {Super-resolution of positive spikes by Toeplitz low-rank approximation},
year = {2015},
pages = {459-463},
abstract = {Super-resolution consists in recovering the fine details of a signal from low-resolution measurements. Here we con sider the estimation of Dirac pulses with positive amplitudes at arbitrary locations, from noisy lowpass-filtered samples. Maximum-likelihood estimation of the unknown parameters amounts to a difficult nonconvex matrix problem of structured low rank approximation. To solve it, we propose a new heuristic iterative algorithm, yielding state-of-the-art results.},
keywords = {approximation theory;concave programming;filtering theory;iterative methods;low-pass filters;matrix algebra;maximum likelihood estimation;signal resolution;signal sampling;Toeplitz low-rank approximation;superresolution positive spike;Dirac pulse estimation;noisy lowpass-filtered sample;maximum-likelihood estimation;parameter estimation;nonconvex matrix problem;structured low rank approximation;heuristic iterative algorithm;Signal processing algorithms;Maximum likelihood estimation;Approximation methods;Noise reduction;Europe;Signal processing;Dirac pulses;sparse spike deconvolution;super-resolution;structured low rank approximation},
doi = {10.1109/EUSIPCO.2015.7362425},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570100837.pdf},
}Downloads: 0
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