Super-Resolution from Noisy Data

Emmanuel J. Candès, Carlos Fernandez-Granda

Research output: Contribution to journalArticlepeer-review

Abstract

This paper studies the recovery of a superposition of point sources from noisy bandlimited data. In the fewest possible words, we only have information about the spectrum of an object in the low-frequency band [-flo,flo] and seek to obtain a higher resolution estimate by extrapolating the spectrum up to a frequency fhi>flo. We show that as long as the sources are separated by 2/flo, solving a simple convex program produces a stable estimate in the sense that the approximation error between the higher-resolution reconstruction and the truth is proportional to the noise level times the square of the super-resolution factor (SRF) fhi/flo.

Original languageEnglish (US)
Pages (from-to)1229-1254
Number of pages26
JournalJournal of Fourier Analysis and Applications
Volume19
Issue number6
DOIs
StatePublished - Dec 2013

Keywords

  • Basis mismatch
  • Deconvolution
  • Line spectra estimation
  • Sparsity
  • Stable signal recovery
  • Super-resolution factor

ASJC Scopus subject areas

  • Analysis
  • Mathematics(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Super-Resolution from Noisy Data'. Together they form a unique fingerprint.

Cite this