Reference-based compressed sensing: A sample complexity approach
Reference-based compressed sensing: A sample complexity approach
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DOI：
10.1109/icassp.2016.7472566
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
J. Mota;L. Weizman;N. Deligiannis;Yonina C. Eldar;M. Rodrigues
中科院分区：
文献类型：
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作者：
J. Mota;L. Weizman;N. Deligiannis;Yonina C. Eldar;M. Rodrigues
我们解决的问题，基于参考的压缩感知：重建稀疏信号从几个线性测量作为先验信息的参考信号，信号类似于我们想要重建的信号。对参考信号的访问出现在诸如医学成像的应用中，通过同一患者的先前图像和压缩视频，其中先前重建的帧可以用作参考。我们的目标是使用参考信号来减少重建所需的测量次数。我们实现这一点，通过一个重新加权的101 - 101最小化方案，更新其权重的基础上，样本的复杂性界限。该方案是简单，直观的，正如我们的实验表明，优于以前的算法，包括重新加权的最小化，和修改CS。
We address the problem of reference-based compressed sensing: reconstruct a sparse signal from few linear measurements using as prior information a reference signal, a signal similar to the signal we want to reconstruct. Access to reference signals arises in applications such as medical imaging, e.g., through prior images of the same patient, and compressive video, where previously reconstructed frames can be used as reference. Our goal is to use the reference signal to reduce the number of required measurements for reconstruction. We achieve this via a reweighted ℓ1-ℓ1 minimization scheme that updates its weights based on a sample complexity bound. The scheme is simple, intuitive and, as our experiments show, outperforms prior algorithms, including reweighted ℓ1 minimization, ℓ1-ℓ1 minimization, and modified CS.