Binary Compressive Sensing via Smoothed ${\ell }_0$ Gradient Descent

Abstract

We present a Compressive Sensing algorithm for reconstructing binary signals from its linear measurements. The proposed algorithm minimizes a non-convex cost function expressed as a weighted sum of smoothed ${\ell }_0$ norms which takes into account the binariness of signals. We show that for binary signals the proposed algorithm outperforms other existing algorithms in recovery rate while requiring a short run time.

References in corpus (2)

• A fast approach for overcomplete sparse decomposition based on smoothed L0 norm
• Discrete Signal Reconstruction by Sum of Absolute Values