Image Inpainting

Image inpainting began as a digital restoration technique inspired by art conservation, where restorers carefully fill damaged areas of paintings. Early digital methods used texture synthesis and patch matching: copying similar patches from nearby regions. Modern deep learning approaches use encoder-decoder and diffusion models trained on large image datasets to understand semantic context, so a removed person standing on grass is filled with more grass rather than random pixels. The technique powers object removal tools, scratch repair for old photos, and generative fill workflows. Quality depends on the size of the masked region, background complexity, edge detail, and the model's training data diversity. Use inpainting on images you own or have permission to edit, especially when removing watermarks, people, or identifying details.