AI image restoration uses neural networks trained on millions of image pairs to reconstruct damaged or deteriorated photographs. The process analyzes surrounding pixels, identifies patterns in texture and structure, then generates plausible content to fill scratches, stains, tears, or missing areas. Modern restoration models combine multiple techniques: edge-aware inpainting for structural elements, texture synthesis for backgrounds, and specialized facial reconstruction for portraits.

Upload your damaged photograph, then select the restoration intensity based on damage severity. For photos with scratches and stains, use standard inpainting mode which analyzes a 128-pixel radius around each damaged area. For images missing large sections (tears, folds), enable structural reconstruction which uses edge detection to rebuild geometric patterns first, then fills textures. For portraits, activate facial landmark detection to ensure eyes, noses, and mouths maintain proper proportions during reconstruction.

Yes, by using edge-aware inpainting that treats scratches as separate objects from the underlying image content. The algorithm first detects scratch boundaries using contrast analysis, then samples texture patterns from adjacent undamaged regions within a 64-256 pixel radius. This preserves original film grain and sharpness because it copies authentic texture rather than applying blur filters. For linear scratches crossing multiple texture zones (like a scratch across both sky and building), segmented inpainting processes each zone separately to maintain distinct textures.

AI restoration handles surface scratches, water stains, emulsion cracks, torn sections, faded areas, and mold spots. Surface damage under 5 pixels wide typically requires single-pass inpainting. Larger tears or missing sections need structural reconstruction that first rebuilds edges and lines, then fills interior textures. Chemical stains that alter color require chromatic correction before inpainting. The most challenging damage is combined deterioration (scratches plus fading plus stains) which needs sequential processing: color correction first, then scratch removal, then detail enhancement.

Enable texture-preserving mode which analyzes grain patterns in undamaged regions, then replicates that grain structure in restored areas. The algorithm samples 32x32 pixel blocks from clean sections to build a grain profile, measuring particle size (typically 1-3 pixels for 35mm film), distribution density, and luminance variance. During inpainting, it applies this grain profile to newly generated pixels so restored sections match the original film stock characteristics. For severely faded photos where grain is barely visible, you can reference grain intensity from the darkest preserved shadow areas.