# Wavelet invariants for statistically robust multi-reference alignment

@inproceedings{Hirn2019WaveletIF, title={Wavelet invariants for statistically robust multi-reference alignment}, author={Matthew Hirn and Anna Little}, year={2019} }

We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be… CONTINUE READING

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