论文标题
插件学习的高斯混合近似消息传递
Plug-And-Play Learned Gaussian-mixture Approximate Message Passing
论文作者
论文摘要
深度展开显示是加速和调整经典信号处理算法的非常成功的方法。在本文中，我们提出了学习的高斯混合放大器（L-GM-AMP） - 插件压缩传感（CS）恢复算法适用于任何I.I.D.来源先验。我们的算法建立在Borgerding的学识渊博的放大器（LAMP）上，但通过在算法中采用普遍的Denoising功能来显着改善它。坚固而柔性的DeNoiser是与高斯混合物（GM）进行建模源的副产品，可以很好地近似连续，离散和混合物分布。使用标准反向传播算法学习其参数。为了证明拟议算法的鲁棒性，我们为混合物和离散分布进行了蒙特卡洛（MC）模拟。数值评估表明，L-GM-AMP算法在没有任何资源的情况下实现了最先进的性能。
Deep unfolding showed to be a very successful approach for accelerating and tuning classical signal processing algorithms. In this paper, we propose learned Gaussian-mixture AMP (L-GM-AMP) - a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. The robust and flexible denoiser is a byproduct of modelling source prior with a Gaussian-mixture (GM), which can well approximate continuous, discrete, as well as mixture distributions. Its parameters are learned using standard backpropagation algorithm. To demonstrate robustness of the proposed algorithm, we conduct Monte-Carlo (MC) simulations for both mixture and discrete distributions. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
