Super-Resolution from Noisy Data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12

AUTHORS

Emmanuel J. Candès, Carlos Fernandez-Granda

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. More... »

PAGES

1229-1254

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00041-013-9292-3

DOI

http://dx.doi.org/10.1007/s00041-013-9292-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033197525


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