论文标题
受限的结构随机矩阵用于压缩感应
Restricted Structural Random Matrix for Compressive Sensing
论文作者
论文摘要
压缩传感（CS）以其独特的感应，压缩和安全性（即CS测量值同样重要）而闻名。但是，有一个权衡。通过先前的信号信息提高感应和压缩效率倾向于采用特定的测量，从而降低了安全性。这项工作旨在通过新颖的采样矩阵（称为限制的结构随机矩阵（RSRM））来提高感应和压缩效率，而不会损害安全性。 RSRM统一了基于框架和基于块的传感的优势以及全局平滑度之前（即低分辨率信号高度相关）。 RSRM获得了多个随机亚采样信号的随机投影（同样重要）的压缩测量，该信号被限制为低分辨率信号（能量相等），因此，其观察值同样重要。事实证明，RSRM可以满足受限的等轴测特性，并显示出可比的重建性能，并具有最新的最新压缩感和基于深度学习的方法。
Compressive sensing (CS) is well-known for its unique functionalities of sensing, compressing, and security (i.e. CS measurements are equally important). However, there is a tradeoff. Improving sensing and compressing efficiency with prior signal information tends to favor particular measurements, thus decrease the security. This work aimed to improve the sensing and compressing efficiency without compromise the security with a novel sampling matrix, named Restricted Structural Random Matrix (RSRM). RSRM unified the advantages of frame-based and block-based sensing together with the global smoothness prior (i.e. low-resolution signals are highly correlated). RSRM acquired compressive measurements with random projection (equally important) of multiple randomly sub-sampled signals, which was restricted to be the low-resolution signals (equal in energy), thereby, its observations are equally important. RSRM was proven to satisfies the Restricted Isometry Property and shows comparable reconstruction performance with recent state-of-the-art compressive sensing and deep learning-based methods.
