One-Bit Compressive Sensing: Can We Go Deep and Blind?
One-Bit Compressive Sensing: Can We Go Deep and Blind?
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DOI：
10.1109/lsp.2022.3187318
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发表时间：
2022-03
影响因子：
3.9
通讯作者：
Yiming Zeng;Shahin Khobahi;M. Soltanalian
中科院分区：
文献类型：
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作者：
Yiming Zeng;Shahin Khobahi;M. Soltanalian
一位压缩感知涉及从其一位噪声测量中准确恢复感兴趣的底层稀疏信号。针对该问题的传统信号恢复方法主要是基于可以获得传感矩阵的精确知识的假设而开发的。然而，在这项工作中，我们提出了一种新颖的数据驱动和基于模型的方法，可以实现盲目恢复；即无需了解传感矩阵即可恢复信号。为此，我们利用深度展开技术，开发了专为该特定任务设计的模型驱动的深度神经架构。所提出的深度架构能够通过利用底层展开算法来学习替代传感矩阵，使得所得到的学习恢复算法能够准确而快速地（就迭代次数而言）从其一位噪声测量中恢复感兴趣的底层压缩信号。此外，由于将领域知识和系统的数学模型纳入所提出的深度架构中，与当前问题常用的黑盒深度神经网络替代方案相比，所得网络受益于增强的可解释性，具有非常少量的可训练参数，并且需要非常少量的训练样本。
One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery; i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.