Research on image inpainting methods based on machine learning

Abstract

The technique of restoring sections of a picture that have been lost or damaged is known as "image inpainting." In light of recent developments in machine learning, academics have begun investigating the possibility of using deep learning methods to the process of picture inpainting. However, the current body of research does not include a comprehensive review of the many different inpainting methods that are based on machine learning, nor does it compare and contrast these methods. This article provides an overview of some of the most advanced and common machine learning based image restoration techniques that are currently available. These techniques include Multivariate inpainting technology and Unit inpainting technology, such as Context-Encoder Network, Generative Adversarial Network (GAN), and U-Net Network. We examine not just the benefits and drawbacks of each method, but also the ways in which it might be used in a variety of settings. At the conclusion of the piece, we predict that machine learning-based inpainting will continue to gain popularity and application in the years to come.