Corpus ID: 235731736

Unbiasing Procedures for Scale-invariant Multi-reference Alignment

@article{Hirn2021UnbiasingPF,
  title={Unbiasing Procedures for Scale-invariant Multi-reference Alignment},
  author={Matthew J. Hirn and Anna V. Little},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.01274}
}
This article discusses a generalization of the 1dimensional multi-reference alignment problem. The goal is to recover a hidden signal from many noisy observations, where each noisy observation includes a random translation and random dilation of the hidden signal, as well as high additive noise. We propose a method that recovers the power spectrum of the hidden signal by applying a data-driven, nonlinear unbiasing procedure, and thus the hidden signal is obtained up to an unknown phase. An… Expand

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