Recognition: unknown
Noise2Noise: Learning Image Restoration without Clean Data
read the original 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 sometimes 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, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans -- all corrupted by different processes -- based on noisy data only.
This paper has not been read by Pith yet.
Forward citations
Cited by 7 Pith papers
-
Pyramid Self-contrastive Learning Framework for Test-time Ultrasound Image Denoising
A2A achieves one-shot ultrasound denoising via pyramid self-contrastive learning on sub-aperture signals to disentangle anatomy from noise, yielding large SNR and CNR gains in simulations and in vivo scans.
-
FrequencyCT: Frequency domain pseudo-label generation for self-supervised low-dose CT denoising
FrequencyCT generates pseudo-labels via regional low-frequency anchoring, phase-preserving modulation, and high-frequency mask perturbation for self-supervised low-dose CT denoising.
-
A Mixture of Experts Foundation Model for Scanning Electron Microscopy Image Analysis
A mixture-of-experts transformer foundation model pretrained on diverse SEM images enables generalization across materials and outperforms SOTA on unsupervised defocus-to-focus restoration.
-
TwinSpecNet: Extending APOGEE's chemical reach to low-S/N spectra via empirical paired learning
TwinSpecNet uses empirical paired learning on spectral twins to denoise low-S/N APOGEE spectra and predict stellar parameters and abundances with lower scatter than the standard pipeline.
-
TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
TM-BSN introduces triangular-masked convolutions that align blind spots with diamond-shaped noise correlations from camera demosaicing, enabling stronger self-supervised denoising at full resolution without downsampling.
-
Machine Learning Enhanced Laser Spectroscopy for Multi-Species Gas Detection in Complex and Harsh Environments
Machine learning methods including denoising autoencoders, unsupervised interference mitigation, blind source separation, and certifiable classification are developed and experimentally validated to improve multi-spec...
-
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Unsupervised denoising methods improve faint-source detection in astronomical images from HST and CFHT, with better performance when models are initialized on similar-domain data.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.