Research map / Image generation / Image restoration
Iterative Residual CNNs for Burst Photography Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the model's burst-aware iterative reconstruction instead of the strongest listed baseline | INN · MSR demosaicking, noisy and noise-free linRGB/sRGB | PSNR: performance gains ranging from 0.9 to 1.5 dB | Helped |
| use INN rather than the best competing baseline at each tested noise level | INN · Waterloo burst denoising, σ=10, σ=15, σ=20, and σ=25 | PSNR: 38.71 vs 37.65; 37.36 vs 36.06; 36.24 vs 34.89; 35.28 vs 33.86 | Helped |
| use INN rather than the best competing baseline | INN · Waterloo burst denoising, σ=5 | PSNR: 40.08 vs 40.19 | Hurt |
| use oracle warping matrices instead of ECC-estimated alignment (ablation) | INN · MSR burst demosaicking | PSNR: up to 1.9 dBs | Helped |
| train with 4 or 8 frames and evaluate on longer bursts (ablation) | INN · Waterloo burst denoising, burst sizes 2 to 16 | PSNR | Helped |
| train with 2 frames and evaluate beyond a certain burst size (ablation) | INN · Waterloo burst denoising, burst sizes 2 to 16 | PSNR | Hurt |
Closest papers by meaning
- Burst Denoising With Kernel Prediction Networks (CVPR 2018 · similarity 0.88)
- Burst Image Restoration and Enhancement (CVPR 2022 · similarity 0.88)
- Zero-Shot Burst Restoration via Diffusion MAP Inference with Poisson-Gaussian Noise Likelihood (NeurIPS 2026 · similarity 0.87)
- Deep Burst Super-Resolution (CVPR 2021 · similarity 0.87)
- Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (CVPR 2018 · similarity 0.86)
- Real-World Mobile Image Denoising Dataset with Efficient Baselines (CVPR 2024 · similarity 0.85)
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