Semantic Scholar Open Access 2018 1947 sitasi

Noise2Noise: Learning Image Restoration without Clean Data

J. Lehtinen Jacob Munkberg J. Hasselgren S. Laine Tero Karras +2 lainnya

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Abstrak

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: under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. We show applications in photographic noise removal, denoising of synthetic Monte Carlo images, and reconstruction of MRI scans from undersampled inputs, all based on only observing corrupted data.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (7)

J. Lehtinen

Jacob Munkberg

J. Hasselgren

S. Laine

Tero Karras

M. Aittala

Timo Aila

Format Sitasi

APA MLA BibTeX

Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M. et al. (2018). Noise2Noise: Learning Image Restoration without Clean Data. https://www.semanticscholar.org/paper/02ccfc9b550d381b5df4365a2ae48bb5f7f7578e

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1947×
Sumber Database: Semantic Scholar
Akses: Open Access ✓