Research map / Probabilistic and scientific modeling / Variational and generative sampling
Variational Denoising Network: Toward Blind Noise Modeling and Removal
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| variational noise-modeling objective instead of MSE loss (ablation) | D-Net · SIDD validation | PSNR: 39.01 -> 39.28 | Helped |
| increase eps0^2 to 1e-4 (ablation) | VDN · SIDD validation | PSNR: 39.28 -> 38.89 | Hurt |
| decrease eps0^2 to 1e-8 (ablation) | VDN · SIDD validation | PSNR: 39.28 -> 39.03 | Hurt |
| vary window size p from 5 to 19 (ablation) | VDN · SIDD validation | PSNR: 39.26 -> 39.24 | No clear effect |
| variational inference training vs CBDNet same architecture | S-Net + D-Net · DND benchmark | PSNR: 38.06 -> 39.38 | Helped |
| feed S-Net predicted noise map to FFDNet instead of true level | FFDNet · Set5 Case 1 | PSNR: 30.16 -> 30.15 | No clear effect |
Closest papers by meaning
- Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks (ICLR 2020 · similarity 0.92)
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- GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images (ICLR 2021 · similarity 0.90)
- Image Blind Denoising With Generative Adversarial Network Based Noise Modeling (CVPR 2018 · similarity 0.90)
- Diffusion Priors for Variational Likelihood Estimation and Image Denoising (NeurIPS 2024 · similarity 0.87)
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