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Generative Diffusion Prior for Unified Image Restoration and Enhancement
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Guide the predicted clean image x̃0 instead of the noisy state xt (ablation) | GDP · ImageNet linear restoration tasks | FID: 64.67 -> 38.24; 5.00 -> 2.44; 20.24 -> 16.58; 66.43 -> 37.60 | Helped |
| Add variance Σ to guided denoising (ablation) | GDP-x0 · ImageNet linear restoration tasks | FID: 38.24 -> 41.22; 2.44 -> 50.29; 16.58 -> 22.77; 37.60 -> 38.39 | Hurt |
| Apply guidance shift with the diffusion posterior coefficient (ablation) | GDP-x0 · ImageNet linear restoration tasks | FID: 38.24 -> 44.16; 2.44 -> 10.35; 16.58 -> 37.32; 37.60 -> 41.53 | Hurt |
| Use GDP-x0 guidance instead of GDP-xt guidance (ablation) | GDP · LOL low-light enhancement | FID: 238.92 -> 75.16 | Helped |
| Use GDP-x0 guidance instead of GDP-xt guidance (ablation) | GDP · NTIRE HDR recovery | PSNR: 19.36 -> 24.88 | Helped |
Closest papers by meaning
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- Improving Diffusion-Based Image Restoration with Error Contraction and Error Correction (AAAI 2024 · similarity 0.91)
- Learning Diffusion Texture Priors for Image Restoration (CVPR 2024 · similarity 0.90)
- Residual Denoising Diffusion Models (CVPR 2024 · similarity 0.90)
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