Fast Binary Compressive Sensing via ℓ_0 Gradient Descent

01/30/2018

∙

by   Tianlin Liu, et al.

∙

0

∙

share

We present a fast Compressive Sensing algorithm for the reconstruction of binary signals 0,1-valued binary signals from its linear measurements. The proposed algorithm minimizes a non-convex penalty function that is given by a weighted sum of smoothed ℓ_0 norms under the [0,1] box-constraint. It is experimentally shown that the proposed algorithm is not only significantly faster than linear-programming-based convex optimization algorithms, but also shows a better recovery performance under several different metrics.