Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit

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.

Accepted by ICASSP 2014

References in corpus (2)

• Robust 1-bit Compressive Sensing via Gradient Support Pursuit
• A Fast Iterative Algorithm for Recovery of Sparse Signals from One-Bit Quantized Measurements

Cited by in corpus (1)

• Fast Adaptive Algorithm for Robust Evaluation of Quality of Experience