A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements
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We propose a novel deep neural network, coined DeepFPC-ℓ_2, for solving the 1-bit compressed sensing problem. The network is designed by unfolding the iterations of the fixed-point continuation (FPC) algorithm with one-sided ℓ_2-norm (FPC-ℓ_2). The DeepFPC-ℓ_2 method shows higher signal reconstruction accuracy and convergence speed than the traditional FPC-ℓ_2 algorithm. Furthermore, we compare its robustness to noise with the previously proposed DeepFPC network—which stemmed from unfolding the FPC-ℓ_1 algorithm—for different signal to noise ratio (SNR) and sign-flipped ratio (flip ratio) scenarios. We show that the proposed network has better noise immunity than the previous DeepFPC method. This result indicates that the robustness of a deep-unfolded neural network is related with that of the algorithm it stems from.
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