Noise2Noise: Learning image restoration without clean data

Loading...
Thumbnail Image

Access rights

openAccess

URL

Journal Title

Journal ISSN

Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2018-01-01

Major/Subject

Mcode

Degree programme

Language

en

Pages

12
4620-4631

Series

35th International Conference on Machine Learning, ICML 2018, Volume 7, Proceedings of Machine Learning Research, issue 80

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.

Description

Keywords

Other note

Citation

Lehtinen, J, Munkberg, J, Hasselgren, J, Laine, S, Karras, T, Aittala, M & Aila, T 2018, Noise2Noise : Learning image restoration without clean data . in J Dy & A Krause (eds), 35th International Conference on Machine Learning, ICML 2018 . vol. 7, Proceedings of Machine Learning Research, no. 80, International Machine Learning Society, pp. 4620-4631, International Conference on Machine Learning, Stockholm, Sweden, 10/07/2018 .