Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent
This addresses the need for efficient binary signal recovery in compressive sensing applications, but it appears incremental as it builds on smoothed ℓ0 norms for a specific signal type.
The paper tackled the problem of reconstructing binary signals from linear measurements in compressive sensing, and the result was that the proposed algorithm achieved higher recovery rates with shorter run times compared to existing methods.
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.