Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit

Xiangrong Zeng; Mário A. T. Figueiredo

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arXiv · 1402.5076

Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit

Xiangrong Zeng·Mário A. T. Figueiredo

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Abstract

We propose a new method, {\it robust binary fused compressive sensing} (RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements. The proposed method is a modification of our previous {\it binary fused compressive sensing} (BFCS) algorithm, which is based on the {\it binary iterative hard thresholding} (BIHT) algorithm. As in BIHT, the data term of the objective function is a one-sided $\ell_1$ (or $\ell_2$) norm. Experiments show that the proposed algorithm is able to take advantage of the piece-wise smoothness of the original signal and detect sign flips and correct them, achieving more accurate recovery than BFCS and BIHT.

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BibTeX RIS

Xiangrong Zeng, Mário A. T. Figueiredo. 2014-03-20. Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit. https://arxiv.org/abs/1402.5076

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