Title :
Nonuniform sparse recovery with random convolutions
Author :
James, David ; Rauhut, Holger
Author_Institution :
Inst. for Numerical & Appl. Math., Univ. of Goettingen, Goettingen, Germany
Abstract :
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.
Keywords :
compressed sensing; convolution; probability; N-dimensional s-sparse signal; Rademacher sequence; Steinhaus sequence; compressed sensing applications; nonuniform sparse recovery; random convolutions; Compressed sensing; Convolution; Electronic mail; Random variables; Sparse matrices; Upper bound;
Conference_Titel :
Sampling Theory and Applications (SampTA), 2015 International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/SAMPTA.2015.7148845
