Blind Deconvolution with Re-weighted Sparsity Promotion

@article{Krishnan2013BlindDW,
  title={Blind Deconvolution with Re-weighted Sparsity Promotion},
  author={Dilip Krishnan and Joan Bruna and Rob Fergus},
  journal={CoRR},
  year={2013},
  volume={abs/1311.4029}
}
Blind deconvolution has made significant progress in the past decade. Most successful algorithms are classified either as Variational or Maximum a-Posteriori (MAP ). In spite of the superior theoretical justification of variational techniques, carefully constructed MAP algorithms have proven equally effective in practice. In this paper, we show that all successful MAP and variational algorithms share a common framework, relying on the following key principles: sparsity promotion in the gradient… CONTINUE READING

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