图像修复研究进展综述

Survey on Image Inpainting Research Progress

图像修复是计算机视觉领域中极具挑战性的研究课题。近年来,深度学习技术的发展推动了图像修复性能的显著提升,使得图像修复这一传统课题再次引起了学者们的广泛关注。文章致力于综述图像修复研究的关键技术。由于深度学习技术在解决“大面积缺失图像修复”问题时具有重要作用并带来了深远影响,文中在简要介绍传统图像修复方法的基础上,重点介绍了基于深度学习的修复模型,主要包括模型分类、优缺点对比、适用范围和在常用数据集上的性能对比等,最后对图像修复潜在的研究方向和发展动态进行了分析和展望。

Image inpainting is a challenging research topic in the field of computer vision.In recent years,the development of deep learning technology has promoted the significant improvement in the performance of image inpainting,which makes image inpainting a traditional subject attracting extensive attention from scholars once again.This paper is dedicated to review the key technologies of image inpainting research.Due to the important role and far-reaching impact of deep learning technology in solving“large-area missing image inpainting”,this paper briefly introduces traditional image inpainting methods firstly,then focuses on inpainting models based on deep learning,mainly including model classification,comparison of advantages and disadvantages,scope of application and performance comparison on commonly used datasets,etc.Finally,the potential research directions and development trends of image inpainting are analyzed and prospected.