Survey of Research on Image Inpainting Methods

Abstract

Image inpainting refers to restoring the pixels in damaged areas of an image to make them as consistent as possible with the original image. Image inpainting is not only crucial in the computer vision tasks, but also serves as an important cornerstone of other image processing tasks. However, there are few researches related to image inpainting. In order to better learn and promote the research of image inpainting tasks, the classic image inpainting algorithms and representative deep learning image inpainting methods in the past ten years are reviewed and analyzed. Firstly, the classical traditional image inpainting methods are briefly summarized, and divided into partial differential equation-based and sample-based image inpainting methods, and the limitations of traditional image methods are further analyzed. Deep learning image inpainting methods are divided into single image inpainting and pluralistic image inpainting according to the number of output images of the model, and different methods are analyzed and summarized in combination with application images, loss functions, types, advantages, and limitations. After that, the commonly used datasets and quantitative evaluation indicators of image inpainting methods are described in detail, and the quantitative data of image inpainting methods to inpaint damaged areas of different areas on different image datasets are given. According to the quantitative data, the performance of image inpainting methods based on deep learning is compared and analyzed. Finally, the limitations of existing image inpainting methods are summarized and analyzed, and new ideas and prospects for future key research directions are proposed.

Keywords

• |computer vision|image inpainting|deep learning|single image inpainting|pluralistic image inpainting