论文标题
从嘈杂的二进制测量值中稀疏信号恢复的强大深度展开的网络
A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements
论文作者
论文摘要
我们提出了一个新颖的深神经网络，即deepfpc-$ \ ell_2 $，用于解决1位压缩感应问题。该网络的设计是通过使用单面$ \ ell_2 $ -norm（fpc-$ \ $ \ ell_2 $）的定点延续（FPC）算法的迭代设计。 DEEPFPC-$ \ ell_2 $方法比传统的FPC-$ \ ell_2 $算法显示更高的信号重建精度和收敛速度。此外，我们将其稳健性与先前提出的DEEPFPC网络进行比较，这是由于展开FPC-$ \ ell_1 $算法的展开而导致的，以换取不同的信号与噪声比（SNR）和符号范围（SNR）和签名比率（FLIP比率）方案。我们表明，所提出的网络比以前的DEEPFPC方法具有更好的噪声免疫力。该结果表明，深度折叠神经网络的鲁棒性与它源自的算法相关。
We propose a novel deep neural network, coined DeepFPC-$\ell_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 $\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signal reconstruction accuracy and convergence speed than the traditional FPC-$\ell_2$ algorithm. Furthermore, we compare its robustness to noise with the previously proposed DeepFPC network---which stemmed from unfolding the FPC-$\ell_1$ 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.
