随机卷积的非均匀稀疏恢复

2015 International Conference on Sampling Theory and Applications (SampTA) Pub Date : 2015-05-25 DOI:10.1109/SAMPTA.2015.7148845

David James, H. Rauhut

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引用次数: 2

摘要

我们讨论了随机卷积在压缩感知应用中的应用。特别是，我们将证明，在将N维s-稀疏信号与Rademacher或Steinhaus序列进行卷积后，它可以通过仅使用m≥s log(N/ε)任意选择的样本(概率至少为1- ε)通过11 -最小化来恢复。

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Nonuniform sparse recovery with random convolutions

We discuss the use of random convolutions for Compressed Sensing applications. In particular, we will show that after convolving an N-dimensional, s-sparse signal with a Rademacher or Steinhaus sequence, it can be recovered via l1-minimization using only m ≳ s log(N/ε) arbitrary chosen samples with probability at least 1 - ε.

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2015 International Conference on Sampling Theory and Applications (SampTA)

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