Image Inpainting

Image inpainting began as a digital restoration technique inspired by art conservation, where restorers carefully fill in 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 architectures trained on millions of images to understand semantic context: they know that a removed person standing on grass should be filled with more grass, not random pixels. Diffusion-based inpainting models generate multiple plausible completions and select the most coherent one. The technique powers object removal tools, scratch repair for old photos, and watermark removal. Quality depends on the size of the masked region, complexity of the background, and the model's training data diversity.