Research map / Image generation / Image restoration
From Noise Modeling to Blind Image Denoising
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LR-MoG instead of BM3D for blind denoising | Image denoising · TID2008, AWGN σ=40 | PSNR: 28.02 -> 28.06 | Helped |
| Use LR-MoG instead of NL-Bayes for blind denoising | Image denoising · TID2008, AWGN σ=100 | PSNR: 23.30 -> 23.31 | Helped |
| Use LR-MoG instead of BM3D for blind denoising | Image denoising · BSDS500, AWGN σ=40 | SSIM: 0.867 -> 0.863 | Hurt |
| Use LR-MoG instead of Multiscale | Blind image denoising · TID2008, combined noise | PSNR: 22.12 -> 29.83 | Helped |
| Use LR-MoG instead of Multiscale | Blind image denoising · BSDS500, combined noise | SSIM: 0.802 -> 0.887 | Helped |
Closest papers by meaning
- FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise (CVPR 2021 · similarity 0.85)
- Variational Denoising Network: Toward Blind Noise Modeling and Removal (NeurIPS 2019 · similarity 0.85)
- Masked and Shuffled Blind Spot Denoising for Real-World Images (CVPR 2024 · similarity 0.85)
- On the Posterior Distribution in Denoising: Application to Uncertainty Quantification (ICLR 2024 · similarity 0.85)
- Zero-Shot Blind-spot Image Denoising via Implicit Neural Sampling (CVPR 2025 · similarity 0.85)
- Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models (ICLR 2024 · similarity 0.83)
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