AI old photo restoration uses neural networks trained on millions of historical photographs to automatically detect and repair common forms of damage including scratches, tears, fading, stains, and color shifts. The AI analyzes degradation patterns specific to different photographic processes (albumen prints, gelatin silver, early color film) and reconstructs missing or damaged areas while preserving authentic period characteristics like grain structure and tonal range.

Scan your original photograph at 600 DPI or higher for prints smaller than 5x7 inches, or 300 DPI for larger prints. Upload the scan and the AI will analyze the image for damage types including creases, scratches, fading, and discoloration. Select restoration options such as scratch removal, tear repair, contrast enhancement, or colorization. The AI processes the image in stages, first removing physical damage, then correcting color and tone, and finally enhancing facial details if present. Review the output and adjust intensity sliders if needed before downloading.

AI colorization models trained on paired black-and-white and color photographs from the same era can predict plausible colors based on contextual clues like clothing styles, skin tones, vegetation, and sky regions. Accuracy varies by subject matter: portraits from the 1940s-1960s typically achieve 85% color plausibility because the training data includes many color films from that period. However, colorization of pre-1900 photographs is less accurate since no color reference images exist. You can manually adjust color regions after AI processing to correct specific elements like eye color or fabric hues.

AI restoration works on digital scans, never on your physical photographs. The process is non-destructive and preserves your original scan file. Our models use conservative reconstruction algorithms that fill damaged areas based on surrounding pixel patterns rather than inventing new details. For archival purposes, we recommend keeping both the original unprocessed scan and the AI-restored version. The restoration maintains original resolution and can export in TIFF format with no compression for museum-quality archival storage.

AI restoration handles scratches and creases by analyzing surrounding texture patterns, repairs torn edges through symmetry detection and pattern matching, removes water stains and mold spots using inpainting algorithms, corrects fading by reconstructing contrast curves based on histogram analysis, and eliminates dust and fingerprints through anomaly detection. The AI recognizes damage specific to different photo types: silver mirroring on daguerreotypes, yellowing on albumen prints, color shifts in early Kodachrome slides, and emulsion cracking on gelatin prints. Complex damage like missing corners or large torn sections may require multiple processing passes with manual guidance.