These early techniques relied on local information. Diffusion-based methods propagated neighbouring pixels into the gap, while patch-based methods (like Adobe's early Content-Aware Fill) searched for similar textures within the same image to "clone" them into missing regions.
is a sophisticated image restoration and editing technique used to fill in missing, damaged, or unwanted parts of an image with realistic content that matches the surrounding area. While its roots lie in the ancient manual restoration of physical artwork, modern digital inpainting leverages Artificial Intelligence (AI) to perform complex tasks like object removal, photo repair, and creative scene expansion with nearly imperceptible results. The Evolution: From Paintbrushes to AI inpaint
With the introduction of Generative Adversarial Networks (GANs) and later Diffusion Models , inpainting became "semantically aware". Instead of just copying nearby grass, these models understand that if a person's leg is missing, they should generate a human limb with matching anatomy and lighting. How AI Inpainting Works These early techniques relied on local information
Modern AI inpainting is a multi-step process that relies on powerful generative models to "imagine" what belongs in a void. While its roots lie in the ancient manual
Digital inpainting has undergone a massive transformation, shifting from purely mathematical algorithms to deep-learning-driven systems.