Fast Binary Compressive Sensing via ℓ_0 Gradient Descent
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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.
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