# Practical approximate projection schemes in greedy signal space methods

@article{Garnatz2014PracticalAP, title={Practical approximate projection schemes in greedy signal space methods}, author={Chris Garnatz and Xiaoyi Gu and Alison Kingman and James LaManna and Deanna Needell and Shenyinying Tu}, journal={ArXiv}, year={2014}, volume={abs/1409.1527} }

Compressive sensing (CS) is a new signal acquisition paradigm which shows that far fewer samples are required to reconstruct sparse signals than previously thought. Although most of the literature focuses on signals sparse in a fixed orthonormal basis, recently the Signal Space CoSaMP (SSCoSaMP) greedy method was developed for the reconstruction of signals compressible in arbitrary redundant dictionaries. The algorithm itself needs access to approximate sparse projection schemes, which have… CONTINUE READING

Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

#### Citations

##### Publications citing this paper.

SHOWING 1-4 OF 4 CITATIONS

## Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing

VIEW 1 EXCERPT

CITES BACKGROUND

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 17 REFERENCES

## Greedy Signal Space Methods for incoherence and beyond

VIEW 2 EXCERPTS

## OMP with Highly Coherent Dictionaries

VIEW 1 EXCERPT

## Towards a Mathematical Theory of Super-Resolution

VIEW 1 EXCERPT