Signal processing with the sparseness constraint
- 27 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3 (15206149) , 1861-1864
- https://doi.org/10.1109/icassp.1998.681826
Abstract
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired. Lastly, a review is given of the algorithms that are currently available for computing sparse solutions.Keywords
This publication has 22 references indexed in Scilit:
- A new algorithm for computing sparse solutions to linear inverse problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Measures and algorithms for best basis selectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Deriving algorithms for computing sparse solutions to linear inverse problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithmIEEE Transactions on Signal Processing, 1997
- Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithmElectroencephalography and Clinical Neurophysiology, 1995
- Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brainReviews of Modern Physics, 1993
- Restoration of blurred star field images by maximally sparse optimizationIEEE Transactions on Image Processing, 1993
- Superresolution via Sparsity ConstraintsSIAM Journal on Mathematical Analysis, 1992
- Continuous probabilistic solutions to the biomagnetic inverse problemInverse Problems, 1990
- An Evaluation of Methods for Neuromagnetic Image ReconstructionIEEE Transactions on Biomedical Engineering, 1987