Compressive sensing (CS) is well-known for its unique functionalities of sensing, compressing, and security (i.e. equal importance of CS measurements). However, there is a tradeoff. Improving sensing and compressing efficiency with prior signal information tends to favour particular measurements, thus decreasing security. This work aimed to improve the sensing and compressing efficiency without compromising 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 of multiple randomly sub-sampled signals, which was restricted to low-resolution signals (equal in energy), thereby its observations are equally important. RSRM was proven to satisfy the Restricted Isometry Property and showed comparable reconstruction performance with recent state-of-the-art compressive sensing and deep learning-based methods.

压缩感测（CS）以其独特的感测，压缩和安全功能（即CS测量同等重要）而闻名。但是，需要进行权衡。利用先验信号信息来提高感测和压缩效率趋向于有利于特定测量，从而降低了安全性。这项工作旨在通过一种名为受限结构随机矩阵（RSRM）的新型采样矩阵，在不损害安全性的情况下提高感测和压缩效率。RSRM结合了基于帧和基于块的感知的优势以及全局平滑度先验（即，低分辨率信号高度相关）。RSRM通过对多个随机子采样信号进行随机投影来获得压缩测量值，这些信号仅限于低分辨率信号（能量相等），因此，它的观察同样重要。事实证明，RSRM可以满足“受限等轴测特性”的要求，并具有与最新的最新压缩感测和基于深度学习的方法相当的重建性能。