Inpainting

Inpainting originated in traditional art restoration, where conservators meticulously hand-painted missing sections of damaged paintings. Digital inpainting translates this concept into an automated process. Early algorithms used diffusion equations to propagate surrounding color and texture into missing regions. Modern AI-based inpainting uses deep learning models trained on millions of images to predict what content should fill a gap, considering both local texture patterns and global scene understanding. These models excel at maintaining visual coherence across large filled regions where simple texture copying would produce obvious repetition artifacts.\n\nConsider a scanned family photo from the 1960s with a water stain across one corner. The stain obscures part of the background and a person's shoulder. AI inpainting analyzes the surrounding fabric pattern, skin tones, and background elements to reconstruct what was hidden. The result preserves the era-appropriate style and photographic characteristics of the original image.\n\nInpainting differs from simple cloning because it generates genuinely new content rather than copying from elsewhere in the image. This means it can handle large missing areas where there is no suitable source material to clone. It also avoids the telltale repetition patterns that make clone stamp work detectable.\n\nMagic Eraser relies on AI inpainting to power its one-click object removal. When a user selects an unwanted element, the inpainting model fills the area with contextually appropriate content — generating grass where grass should be, continuing brick patterns, or extending sky gradients. The AI Fill tool further extends inpainting to let users generate entirely new content in selected regions based on surrounding visual context. This combination of removal and generation means users can not only erase unwanted elements but also seamlessly reconstruct complex backgrounds including intricate architectural details, natural landscapes, and repeating patterns that would challenge traditional editing methods.