AI Restoration of Archival Video
Old film scanned at 2K, but the result has scratches, flicker, grain, blurred frames, and faded colors. Classic restoration in DaVinci Resolve requires manual treatment of each defect. We have developed a neural network pipeline that automatically handles 80% of this work, leaving final correction to a human. Order archival video restoration — we will assess your project for free. Our experience: over 50 successful projects, years of practice with AI restoration. This article explains how the pipeline works and which models deliver maximum quality gains.
Which Defects Does the Neural Network Address?
Film suffers from many defects, each requiring its own architecture. Grain and noise — film grain is statistically different from digital, and specialized models (Real-ESRGAN, BSRGAN, NAFNet) remove it without losing textures. Scratches and artifacts — video inpainting via ProPainter or E2FGVI automatically detects and fills defects from neighboring frames while maintaining temporal consistency. Low resolution — upscaling with HAT lifts 480p to 4K, yielding PSNR gains of up to 1.8 dB over Real-ESRGAN. Missing frames — interpolation via RIFE or FILM restores smoothness. Faded colors — temporally stable colorization via DDColor with expert oversight.
Why Is the Order of Operations Critical?
Order is critical — wrong sequence multiplies artifacts. If you upscale before removing grain, Real-ESRGAN amplifies grain, mistaking it for details. Here is the proven scheme:
1. Scratch detection and removal (inpainting) ↓ 2. Denoising / grain removal ↓ 3. Deflickering (normalize brightness between frames) ↓ 4. Resolution upscaling ↓ 5. Frame interpolation (if needed) ↓ 6. Colorization (if black-and-white original) ↓ 7. Final temporal smoothing The most common mistake in "quick" pipelines — skipping deflickering before upscaling leads to flicker artifacts.
Our Case: Documentary Archive
One client — a museum with 60 minutes of documentary material from 16mm film. Heavy grain (equivalent to ISO ~3200), vertical scratches on 15% of frames, intermittent fade-out artifacts. We applied a pipeline with a trained U-Net for scratch detection on synthetic data, ProPainter for inpainting with a temporal window of 10 frames, NAFNet for denoising, HAT-L for upscaling 2K→4K. Colorization was not needed. Processing time on A100 80GB: 4.2 hours for 60 minutes at 1920×1080. Final VMAF rose from 78 to 91, experts confirmed the result. This saved the client 70% of the time compared to manual restoration. We guarantee this approach for your archives.
What Is Included in the Work?
- Analysis of source material: defect assessment, selection of model combination
- Automated processing via a calibrated pipeline, adjusted to content
- Final QA with metrics (VMAF, PSNR, LPIPS) and manual artifact correction
- Pipeline documentation and archiving recommendations
- Possibility of fine-tuning models for your archive's specifics (fine-tuning on your frames)
We guarantee deadlines and quality. Contact us for an assessment — get a free consultation.
Comparison of Approaches
| Technology | Speed (1 hour video) | Quality (VMAF) | Automation |
|---|---|---|---|
| Manual in DaVinci Resolve | 20–40 hours | ~85–90 | 30% |
| Our AI pipeline | 4–8 hours | 78→91 | 80% |
| Hyper-upscaling (only upscaling) | 1 hour | ~70 | 95% |
Real-ESRGAN surpasses traditional bicubic methods in texture restoration quality. We use it after grain removal for maximum detail preservation.
How Does AI Restoration Save Time?
Instead of 20–40 hours of manual work (DaVinci Resolve), our pipeline processes an hour of video in 4–8 hours, raising VMAF from 78 to 91. ProPainter performs frame inpainting in seconds — manual retouching of each frame takes hours. For long archives, this reduces costs by 5–10 times. Additionally, batch inference on A100 allows parallel processing of multiple segments.
Limitations
The neural network cannot restore what is not in the frame — if a frame is completely destroyed, inpainting hallucinates content from neighboring frames. Such frames are flagged for manual verification. Colorization of historical material requires expert oversight — the model does not know the actual colors of past objects. It is important to include this stage in the budget.
Processing Timelines
| Volume | Automated pipeline | With QA and correction |
|---|---|---|
| Short film up to 20 min | 1–2 days | 3–7 days |
| Documentary 60–90 min | 3–5 days | 2–3 weeks |
| Archival collection 10+ hours | 2–3 weeks | 1–2 months |
Cost is calculated individually. Our certified engineers guarantee results. Get a consultation — discuss your archive.