Research map / Image generation / Image restoration
Noise2Void - Learning Denoising From Single Noisy Images
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train with blind-spot masking using single noisy images instead of requiring clean targets or noisy pairs | Noise2Void · simulated microscopy data | denoising quality | No clear effect |
| train with blind-spot masking using single noisy images | Noise2Void · BSD68 | average PSNR | Hurt |
| train with blind-spot masking using single noisy images | Noise2Void · simulated microscopy data | denoising quality | Helped |
| use Noise2Void instead of BM3D | cryo-TEM denoising · cryo-TEM | runtime: about $25$ times faster | Helped |
| use Noise2Void rather than a conventional network that can access the center pixel | Noise2Void · images with highly irregular or unpredictable signal | prediction error | Hurt |
| apply blind-spot training when noise is structured rather than pixel-wise independent | Noise2Void · images with structured noise | structured-noise removal | Hurt |
Closest papers by meaning
- Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images (NeurIPS 2021 · similarity 0.91)
- GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images (ICLR 2021 · similarity 0.90)
- Noisier2Noise: Learning to Denoise From Unpaired Noisy Data (CVPR 2020 · similarity 0.90)
- Zero-Shot Noise2Noise: Efficient Image Denoising Without Any Data (CVPR 2023 · similarity 0.89)
- Zero-Shot Noise2Mean: Gap Minimization for Efficient Denoising from a Single Noisy Image (AAAI 2025 · similarity 0.89)
- Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising (CVPR 2025 · similarity 0.86)
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