Blind Deconvolution From Multiple Sparse Inputs

@article{Wang2016BlindDF,
  title={Blind Deconvolution From Multiple Sparse Inputs},
  author={Liming Wang and Yuejie Chi},
  journal={IEEE Signal Processing Letters},
  year={2016},
  volume={23},
  pages={1384-1388}
}
Blind deconvolution is an inverse problem when both the input signal and the convolution kernel are unknown. We propose a convex algorithm based on $1-minimization to solve the blind deconvolution problem, given multiple observations from sparse input signals. The proposed method is related to other problems such as blind calibration and finding sparse vectors in a subspace. Sufficient conditions for exact and stable recovery using the proposed method are developed that shed light on the sample… CONTINUE READING
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