Research map / Image generation / Image restoration
Estimating Fine-Grained Noise Model via Contrastive Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed testing-image-only noise synthesis pipeline instead of CANGAN | SIDD noise synthesis · SIDD, average across cameras | KL divergence: 0.0220 -> 0.0211 | Helped |
| Use the proposed noise synthesis pipeline instead of paired training data | UNet denoising · SIDD, S6, All | PSNR: 50.10 -> 50.13 | Helped |
| Use the proposed noise synthesis pipeline instead of paired training data | UNet denoising · SIDD, S6, All | SSIM: 0.9902 -> 0.9891 | Hurt |
| Add contrastive loss to the noise estimation model (ablation) | Proposed pipeline · SIDD, S6, All | PSNR: 49.03 -> 50.13 | Helped |
| Add contrastive loss to the noise estimation model (ablation) | Proposed pipeline · SIDD, S6, All | SSIM: 0.9868 -> 0.9891 | Helped |
| Use the fine-grained noise model instead of Hetero-G (ablation) | Proposed pipeline · SIDD, S6, All | PSNR: 50.04 -> 50.13 | Helped |
| Use the fine-grained noise model instead of Hetero-G (ablation) | Proposed pipeline · SIDD, S6, All | SSIM: 0.9874 -> 0.9891 | Helped |
Closest papers by meaning
- Noise2NoiseFlow: Realistic Camera Noise Modeling Without Clean Images (CVPR 2022 · similarity 0.91)
- Realistic Noise Synthesis with Diffusion Models (AAAI 2025 · similarity 0.89)
- sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows (ICLR 2024 · similarity 0.88)
- Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising (CVPR 2025 · similarity 0.88)
- LAN: Learning to Adapt Noise for Image Denoising (CVPR 2024 · similarity 0.87)
- Is Noise Conditioning Necessary for Denoising Generative Models? (ICML 2025 · similarity 0.87)
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