Traditional 2D animation is a distinct visual style with a production process and image characteristics that differ significantly from real-life scenes. It usually requires drawing pictures frame by frame and saving them as bitmaps. During the storage, transmission, and playback process, 2D animation may encounter problems such as picture quality degradation, insufficient resolution, and discontinuous timing. With the development of deep learning technology, it has been widely used in the field of animation restoration. This paper provides a comprehensive summary of 2D animation restoration based on deep learning. Firstly, exploring existing animation datasets can help identify the available data support and the bottleneck in establishing animation datasets. Secondly, investigating and testing deep learning-based algorithms for animation image quality restoration and animation interpolation can help identify key points and challenges in animation restoration. Additionally, introducing methods designed to ensure consistency between animation frames can provide insights for future animation video restoration. Analyzing the effectiveness of existing image quality assessment (IQA) methods for animation images can help identify practical IQA methods to guide restoration results. Finally, based on the above analysis, this paper clarifies the challenges in animation restoration tasks and presents future development directions of deep learning in animation restoration field.