Understanding Image Inpainting

Abstract

Image inpainting is a computer vision technique that aims to fill in missing or damaged areas of an image with plausible content. The goal is to create a visually coherent and realistic image that seamlessly integrates the inpainted regions with the surrounding context. Image inpainting techniques typically utilize nearby information or context to infer the missing or damaged pixels. They can be broadly classified into two categories: exemplar-based inpainting methods and partial differential equation-based inpainting methods. Exemplar-based inpainting methods rely on the idea of finding similar patches or regions in the image to fill in the missing areas. These algorithms search for patches or regions that have similar textures, colors, and structures to the missing region. Once a match is found, the missing pixels are filled in based on the information from the matched patch or region. Partial differential equation-based inpainting methods, on the other hand, formulate the inpainting problem as a minimization of energy functional. This energy functional accounts for both the fidelity to the known information and the smoothness of the inpainted regions. Partial differential equation-based inpainting methods solve the inpainting problem by finding a solution that minimizes the energy functional, resulting in smooth and visually pleasing inpainted images.