A frame theoretic approach to the nonuniform fast fourier transform

Anne Gelb, Guohui Song

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Nonuniform Fourier data are routinely collected in applications such as magnetic resonance imaging, synthetic aperture radar, and synthetic imaging in radio astronomy. To acquire a fast reconstruction that does not require an online inverse process, the nonuniform fast Fourier transform (NFFT), also called convolutional gridding, is frequently employed. While various investigations have led to improvements in accuracy, efficiency, and robustness of the NFFT, not much attention has been paid to the fundamental analysis of the scheme, and in particular its convergence properties. This paper analyzes the convergence of the NFFT by casting it as a Fourier frame approximation. In so doing, we are able to design parameters for the method that satisfy conditions for numerical convergence. Our so-called frame theoretic convolutional gridding algorithm can also be applied to detect features (such as edges) from nonuniform Fourier samples of piecewise smooth functions.

Original languageEnglish (US)
Pages (from-to)1222-1242
Number of pages21
JournalSIAM Journal on Numerical Analysis
Issue number3
StatePublished - 2014


  • Convolutional gridding
  • Fourier frames
  • Nonuniform fast Fourier transform
  • Numerical frame approximation

ASJC Scopus subject areas

  • Numerical Analysis
  • Computational Mathematics
  • Applied Mathematics


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