Research Advanced in Image Inpainting based on Deep Learning

Image inpainting has always been a hotspot in the computer vision community, which aims to restore damaged or missing parts of an image, ensuring that the restored image is physically reasonable, visually pleasing, and consistent in texture and structure with the original image. Early inpainting methods primarily depend on diverse image processing technologies, such as sample replication, partial differential equation solving, etc. As artificial intelligence rapidly progresses, image inpainting methods based on deep learning exploit robust feature extraction capabilities to extract useful information from vast amounts of data, contributing to more robust and natural inpainting effects, especially when dealing with complex and high-dimensional scenes. This paper provides a review of deep learning-based image inpainting technologies, categorized into four main types: autoencoders, generative adversarial networks (GANs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). It summarizes and analyzes the basic principles, advantages, disadvantages, and performance of these methods in image inpainting, as well as introduces commonly used datasets and quantitative evaluation metrics. Additionally, the paper discusses the principal challenges currently faced in this domain and provides insights into future research directions for deep learning-based image inpainting.