Automated Restoration of Degraded Historical Images
The Challenge
Their valuable collection of historical photographs was deteriorating, but professional restoration was a slow, expensive manual process (2+ hours per image). This made large-scale preservation projects impossible, leaving the archives at risk.
Our Solution
Developed a hybrid pipeline combining GAN-based inpainting (to repair damage) and supervised colorization (trained on period-accurate color references). Used a custom loss function that preserves historical authenticity. Implemented a human-in-the-loop system for quality control and fine-tuning. Deployed as a cloud-native service with GPU acceleration.
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Results & Impact
Restored 9,000+ customer images with historical accuracy
Reduced per-image processing time from 2 hours to < 30 seconds
Enabled public access to previously unusable archival material
Achieved 92% customer satisfaction with restoration quality
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