Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem

@article{Adcock2013BeyondCR,
  title={Beyond Consistent Reconstructions: Optimality and Sharp Bounds for Generalized Sampling, and Application to the Uniform Resampling Problem},
  author={B. Adcock and A. Hansen and C. Poon},
  journal={ArXiv},
  year={2013},
  volume={abs/1301.2831}
}
  • B. Adcock, A. Hansen, C. Poon
  • Published 2013
  • Computer Science, Mathematics
  • ArXiv
  • Generalized sampling is a recently developed linear framework for sampling and reconstruction in separable Hilbert spaces. It allows one to recover any element in any finite-dimensional subspace given finitely many of its samples with respect to an arbitrary basis or frame. Unlike more common approaches for this problem, such as the consistent reconstruction technique of Eldar and others, it leads to numerical methods possessing both guaranteed stability and accuracy. The purpose of this paper… CONTINUE READING
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