Somantika Datta, Kangyu Ni, Prasun Mahanti, Svetlana Roudenko
Department of Mathematics, George Washington University, Washington DC, USA.
Department of Mathematics, University of Idaho, Moscow, USA.
HRL Laboratories, LLC, Malibu, USA.
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, USA.
DOI: 10.4236/am.2013.41A029   PDF    HTML   XML   3,465 Downloads   6,173 Views   Citations

Abstract

We explore the stability of image reconstruction algorithms under deterministic compressed sensing. Recently, we have proposed [1-3] deterministic compressed sensing algorithms for 2D images. These algorithms are suitable when Daubechies wavelets are used as the sparsifying basis. In the initial work, we have shown that the algorithms perform well for images with sparse wavelets coefficients. In this work, we address the question of robustness and stability of the algorithms, specifically, if the image is not sparse and/or if noise is present. We show that our algorithms perform very well in the presence of a certain degree of noise. This is especially important for MRI and other real world applications where some level of noise is always present.

Keywords

Compressed Sensing; Reed-Muller Sequences; Chirps; Image Reconstruction

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Conflicts of Interest

The authors declare no conflicts of interest.

References

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