Noise2Noise: Learning Image Restoration without Clean Data
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.
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Colab
Datasets
BSD
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Salt-And-Pepper Noise Removal | BSD300 Noise Level 30% | Noise2Noise | PSNR | 39.83 | # 2 | |
| Salt-And-Pepper Noise Removal | BSD300 Noise Level 50% | Noise2Noise | PSNR | 35.92 | # 2 | |
| Salt-And-Pepper Noise Removal | BSD300 Noise Level 70% | Noise2Noise | PSNR | 31.42 | # 2 | |
| Salt-And-Pepper Noise Removal | Kodak24 Noise Level 30% | Noise2Noise | PSNR | 34.95 | # 2 | |
| Salt-And-Pepper Noise Removal | Kodak24 Noise Level 50% | Noise2Noise | PSNR | 32.27 | # 2 | |
| Salt-And-Pepper Noise Removal | Kodak24 Noise Level 70% | Noise2Noise | PSNR | 30.49 | # 2 |
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