Deep learning for image inpainting: A survey

•We summarize existing deep learning-based image inpainting algorithms in three aspects, including inpainting strategies, network structures and loss functions.•We introduce the open source codes, popular used datasets, evaluation metrics, and application scenarios.•We compare the inpainting algorithms with released source codes.•We outline the challenges and possible future directions. Image inpainting has been widely exploited in the field of computer vision and image processing. The main purpose of image inpainting is to produce visually plausible structure and texture for the missing regions of damaged images. In the past decade, the success of deep learning has brought new opportunities to many vision tasks, which promoted the development of a large number of deep learning-based image inpainting methods. Although these methods have many similarities, they also have their own characteristics due to the differences in data types, application scenarios, computing platforms, etc. It is necessary to classify and summarize these methods to provide a reference for the research community. In this survey, we present a comprehensive overview of recent advances in deep learning-based image inpainting. First, we categorize the deep learning-based techniques from multiple perspectives: inpainting strategies, network structures, and loss functions. Second, we summarize the open source codes and representative public datasets, and introduce the evaluation metrics for quantitative comparisons. Third, we summarize the real-world applications of image inpainting in different scenarios, and give a detailed analysis on the performance of different inpainting algorithms. At last, we conclude the survey and discuss about the future directions.