A Survey on Greedy Reconstruction Algorithms in Compressive Sensing
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2016, Vol 5, Issue 3
Abstract
Compressive sensing (CS) is a field of signal processing that provides a framework for image recovery using sub-Nyquist sampling rates. CS has recently gained a lot of attention due to its exploitation of signal sparsity. It is an inherent characteristic of many natural signals. The signals can be stored in few samples and recovered accurately. In this survey, we consider various greedy algorithms that are used to reconstruct the original signal from the samples. Matching pursuit is a type of sparse approximation which involves finding the "best matching" projections of multidimensional data onto an over-complete dictionary. The main extensions of MP algorithms are OMP, St-OMP and ROMP.
Authors and Affiliations
Akhila Arjunan T, Divya R
Keywords
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