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.

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}, }

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