压缩传感理论主要包括信号的稀疏表示、编码测量和重构算法等三个方面。
压缩传感 ( Compressive Sensing )作为近几年来兴起的信息处理技术，突破了信号采样过程中的最低采样频率，即采样定理的限制。
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Compressed sensing theory presents a new method to capture and compress the data, which can reconstruct the original signal from much fewer measurements using the prior knowledge that the signal has the sparse representation.
压缩传感理论给出了一种新的信息获取和压缩的方法,其本质是利用信号可稀疏表示的先验知识,从比奈奎斯特采样少的多的观测值中重构原始信号。
参考来源 - 基于双树复数小波的压缩传感图像重构算法研究Fourthly, compressed sensing theory is analyzed.
第四,分析了压缩传感理论。
参考来源 - 低快拍数下稳健波束形成技术的研究·2,447,543篇论文数据，部分数据来源于NoteExpress
基于压缩传感的MRI图像重构利用图像稀疏的先验知识能从很少的投影值重构原图像。
The reconstruction of MRI based on compressed sensing is able to recover the original image from the fewer projections using the sparse priors of image.
针对压缩域视频流的完整性认证问题，提出了一种基于压缩传感(CS)的视频水印算法。
For the problem of integrity authentication about compressed video streams, this paper proposed a video watermarking algorithm based on Compressive Sensing (CS).
目前在压缩传感重构算法中利用图像的可稀疏性表示先验知识，从比奈奎斯特采样少得多的观测值中恢复原始图像。
The current image compressed sensing algorithms can reconstruct the original image using the sparse prior of image from far fewer measurements than the Nyquist samples.
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