A Comprehensive Review of Generative Adversarial Network (GAN) based Image Inpainting

Image inpainting is a method that uses AI algorithms to recreate missing picture parts in order to "fix" or "correct" an image. That's because it "fixes" or "corrects" the original photo. As image inpainting techniques have progressed and gotten more complex, the significance of Image Inpainting algorithms has grown in recent years. It may be used to edit images in several ways, including erasing unwanted elements, reducing background noise, and adding missing details. To do this, it is required to present an overview of picture inpainting techniques that may serve as a thorough introduction to the field. Although there are insufficient existing resources that offer a full review of all the image Inpainting techniques that are now in use, this investigation was conducted. As the area of image inpainting develops, researchers may want documentation detailing the many image inpainting training techniques available for use while developing their models. In order to aid newcomers in learning more about image Inpainting, this study provides a comprehensive overview of the topic. The major goal of this research was to synthesize existing work on image Inpainting by summarizing the various methods used and the domains in which they were used