Noise2Noise: Learning image restoration without clean data

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Nvidia
  • Massachusetts Institute of Technology

Abstract

We apply basic statistical reasoning to signal reconstruction by machine learning - learning to map corrupted observations to clean signals - with a simple and powerful conclusion: It is possible to learn to restore images by only looking at corrupted examples, at performance at and some-times exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denois- ing synthetic Monte Carlo images, and reconstruction of undersampled MRI scans - all corrupted by different processes - based on noisy data only.

Details

Original languageEnglish
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsJennifer Dy, Andreas Krause
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018
Conference number: 35

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Number80
ISSN (Electronic)1938-7228

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountrySweden
CityStockholm
Period10/07/201815/07/2018

ID: 31217122