深度学习的二维动画视觉领域修复综述

Review of 2D Animation Restoration in Visual Domain Based on Deep Learning

传统二维动画是一种具有独特风格的影像作品,其制作方式和画面特征与现实场景的影像存在很大差异。传统二维动画通常需要逐帧绘制图片,并保存为位图。但是二维动画在存储、传输和播放过程中,可能会出现画质退化、分辨率不足、时序不连续等问题。随着深度学习技术的发展,基于深度学习的方法在动画修复领域得到了广泛应用。对基于深度学习的二维动画修复进行了全面总结。首先,调研了目前与动画相关的数据集,明确了深度学习在动画修复领域已有的数据支撑以及动画数据集建立的瓶颈。其次,通过调研和测试动画画质修复与动画视频插帧的深度学习算法,找出了目前动画修复的关键点与难点,介绍了一些针对动画画面特征设计的保证动画帧间一致性的方法,为动画视频的修复提供了思路。然后,通过分析现有图像质量评价方法对动画影像的有效性,找到能合理评价和指导动画修复结果的指标。最终,根据上述分析,阐明了动画相关修复任务中面临的挑战,并提出了未来深度学习在动画修复领域的发展方向。

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.