# A Review of Image Inpainting Methods Based on Deep Learning
## 1 Introduction  
Image processing technology is a crucial research direction of deep learning in the field of computer vision. Image inpainting, as a significant branch of image processing, has been widely applied in various industries, such as medical image processing [1], satellite remote sensing image processing [2; 3; 4; 5], and image processing for film and artistic creation [6; 7; 8; 9]. During image processing, factors such as a poor environment, excessive noise, unfavorable shooting conditions, and unstable network communications often result in image blurring and loss. For instance, in the realm of satellite remote sensing, weather conditions and cloud cover severely affect the image quality, leading to the loss of some key information, such as color features, texture characteristics, and semantic details, which can significantly impact the quality and integrity of the image. In research on ancient murals and artifacts, murals from different periods and scenarios exhibit varying levels of damage and loss. When handling other computer vision tasks, like object identification, object detection, semantic segmentation, human pose estimation, and gait recognition, the subjects in images are often obstructed by unnecessary objects, significantly reducing accuracy. Therefore, image inpainting can be utilized to fill in and repair missing and damaged images and to remove and replace unwanted objects in images awaiting processing. Other technicalmethods such as image segmentation, object detection, and image enhancement are also employed when necessary. The history of image inpainting technology is long-standing. As early as the Renaissance, artists manually restored damaged murals, calligraphy, and other artworks using their thoughts and painting skills. This manual inpainting was influenced by many uncertain human factors, such as the professional integrity of the restorer and the personal state of the restorer during the repair, making it time-consuming and unable to guarantee inpainting quality. With the evolution of computer digital imaging technology, some early traditional algorithms were used to effectively repair minor damage like scratches using partial differential equations [10], sample-based image inpainting models [11], variational inpainting based on geometric image models [12], texture synthesis [13], and data-driven [14] methods. With the abundance of computational resources and the advancement of artificial intelligence, a series of deep-learning-based image inpainting methods [15] increasingly emphasized their advantages in image semantic feature extraction [16], image transformation [17], and image generation [18], thus promoting the development of image inpainting based on deep neural networks. Image inpainting is a prominent research direction in deep learning. Deep-learning-based image inpainting techniques take the image to be repaired as input [19; 20; 21; 22]. They utilize the known part of the image information to calculate the pixel information of the area to be repaired. A 'trained model' refers to a neural network model that has been previously trained on a large dataset to understand and generate image patterns. The known information includes the image's color information, texture feature information, and semantic information. Unlike deep-learning-based methods, traditional image inpainting algorithms typically focus solely on the first two aspects. When the area of missing data in an image is extensive, traditional algorithms often struggle to provide accurate inpainting results that align with expectations. Consider a scenario where a significant portion of a wall in an image is missing, potentially hiding features like a window or a door. Traditional inpainting algorithms primarily rely on the pixels immediately surrounding the missing area, which might not always capture potential features like windows or doors. In contrast, deep-learning-based inpainting techniques leverage the generative capabilities of models, allowing for a broader range of inpainting possibilities [23].