Papers › Noise2Self: Blind Denoising by Self-Supervision
Noise2Self: Blind Denoising by Self-Supervision
Joshua Batson, Loic Royer
We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only assumption is that the noise exhibits statistical independence across different dimensions of the measurement, while the true signal exhibits some correlation. For a broad class of functions ("𝒥-invariant"), it is then possible to estimate the performance of a denoiser from noisy data alone. This allows us to calibrate 𝒥-invariant versions of any parameterised denoising algorithm, from the single hyperparameter of a median filter to the millions of weights of a deep neural network. We demonstrate this on natural image and microscopy data, where we exploit noise independence between pixels, and on single-cell gene expression data, where we exploit independence between detections of individual molecules. This framework generalizes recent work on training neural nets from noisy images and on cross-validation for matrix factorization.
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Code
Syntology Ran 3 of 20 code samples harvested from 2 repositories linked to this paper; 17 have no recorded run. Of those that ran: 3 ran · our draft was wrong.
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Code Syntology ran Syntology
20 samples harvested; 3 ran; 0 honoured the contract we drafted; 17 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Color Image Denoising | CellNet | DnCNN (n2t) | PSNR | 34.4 | #1 of 1 | Archive leaderboard | report |
| Color Image Denoising | Hanzi | DnCNN (n2t) | PSNR | 13.9 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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