基于深度学习的图像修复方法研究综述

彭进业; 余喆; 屈书毅; 胡琦瑶; 王珺

doi:10.16152/j.cnki.xdxbzr.2023-06-006

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基于深度学习的图像修复方法研究综述

西北大学学报110周年刊庆 | 更新时间：2024-01-03

- 基于深度学习的图像修复方法研究综述
- A survey of image inpainting methods based on deep learning
- 西北大学学报（自然科学版） 2023年53卷第6期页码：943-963
- 作者机构：
  
  1.西北大学　信息科学与技术学院，陕西　西安　710127
  2.陕西省丝绸之路文化遗产数字化保护与传承协同创新中心，陕西　西安　710127
- 作者简介：
  
  [ "彭进业，男，1964年生，博士，二级教授、博士生导师，文化遗产数字化保护与传播教育部创新团队负责人(带头人)，文化遗产数字化保护陕西省三秦学者创新团队负责人(带头人)，教育部新世纪优秀人才，陕西省三秦学者，陕西省教学名师，全国高校黄大年式教师团队核心成员(排名第二)。现任西北大学信息科学与技术学院院长、软件学院院长，新型网络智能信息服务国家地方联合工程研究中心常务副主任，文化遗产数字化国家地方联合工程研究中心副主任，陕西省面向领域应用的人工智能技术学科创新引智基地主任，陕西省丝绸之路文化遗产数字化保护与传承协同创新中心主任。兼任中国计算机自动测量与控制技术协会理事、陕西省图象图形学学会副理事长等职。先后担任IoTaas 2020、CBD 2021、ICIPMC 2022/2023等多个国际学术会议主席。从事智能信息处理、文物数字化保护技术等方面的研究与教学工作。主持国家重点研发课题、国家自然科学基金等科研项目20多项。曾在日本国立Toyohashi科技大学作访问学者。在IEEE TIP、TMM、TKDE、TITB、Journal of Cultural Heritage、《中国科学》等国内外刊物及CVPR、WWW、IJCAI等重要国际学术会议上发表一批学术论文，获授权发明专利20多项，其中转化应用7项，获国家教学成果二等奖、陕西省科学技术二等奖等教学科研奖励。" ]
- 基金信息：
  
  国家自然学科基金(62101446);陕西省科技计划重点项目(2021ZDLGY15-06);陕西省自然科学基金(2023-JC-QN-0750)
- DOI：10.16152/j.cnki.xdxbzr.2023-06-006
  中图分类号：
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彭进业, 余喆, 屈书毅, 等. 基于深度学习的图像修复方法研究综述[J]. 西北大学学报（自然科学版）, 2023,53(6):943-963.

PENG Jinye, YU Zhe, QU Shuyi, et al. A survey of image inpainting methods based on deep learning[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):943-963.
彭进业, 余喆, 屈书毅, 等. 基于深度学习的图像修复方法研究综述[J]. 西北大学学报（自然科学版）, 2023,53(6):943-963. DOI： 10.16152/j.cnki.xdxbzr.2023-06-006.

PENG Jinye, YU Zhe, QU Shuyi, et al. A survey of image inpainting methods based on deep learning[J]. Journal of Northwest University (Natural Science Edition), 2023,53(6):943-963. DOI： 10.16152/j.cnki.xdxbzr.2023-06-006.

摘要

图像修复是指通过使用计算机算法和图像处理技术还原损坏、缺失或被破坏的图像区域，其目标是使修复后的图像在视觉上具有合理的结构、纹理和连贯性，并且尽可能与原始图像的外观和信息接近。传统的图像修复技术通常基于规则和启发式方法，利用像素间的局部关系、边缘信息、纹理统计等低级特征进行图像修复，难以修复具有复杂语义的图像。近年来，深度学习技术由于其强大的特征提取能力，在图像修复任务中逐渐成为主流方法。这些方法借助大规模数据集进行训练，通过深层次的卷积神经网络或生成对抗网络自动学习图像的高级特征和复杂语义信息。然而，现有的图像修复总结研究较少，且深度学习技术更新太快，为了更好地推动深度学习技术在图像修复领域中的应用及发展，有必要对现有相关方法进行分类和总结。该文对基于深度学习的图像修复方法进行了系统回顾和全面概述，从修复策略的角度出发对图像修复方法进行系统性总结。具体分析了每类方法的优势和局限性，总结了常用的数据集、定量评价指标及代表性方法的性能对比，对图像修复领域存在的难点问题及未来研究方向进行了展望。

Abstract

Image inpainting is a process that involves utilizing computer algorithms and image processing techniques to restore damaged, missing, or corrupted regions within an image. The objective of this process is to generate visually reasonable and coherent structures and textures in the repaired regions, while simultaneously being as consistent as possible with the appearance of the original image. Traditional image inpainting techniques predominantly rely on rule-based and heuristic methods, utilizing low-level features such as local pixel relationships, edge information, and texture statistics to perform inpainting tasks. However, handling images with intricate semantics through these methods has proven challenging. In recent years, the prominence of deep learning technology has grown significantly in image inpainting tasks owing to its powerful feature extraction capabilities. By leveraging large-scale datasets, these methods automatically learn high-level features and complex semantic information of images through deep convolutional neural networks or generative adversarial networks. However, there are few existing summary studies on image inpainting, while the evolution of deep learning technology is progressing rapidly. In order to facilitate the effective application and development of deep learning methods in image inpainting, a systematic categorization and summary of existing techniques is imperative. This article provides a systematic review and comprehensive overview of deep learning-based image restoration methods, offering a systematic summary of image inpainting methods from the perspective of inpainting strategies. We specifically analyzed the strengths and limitations of each method category, summarized commonly used datasets, quantitative evaluation metrics, and performance comparisons of representative approaches. Ultimately, we discussed the existing challenges in the field of image inpainting and proposed potential research avenues for future investigations.

关键词

数字图像处理图像修复深度学习计算机视觉

Keywords

digital image processingimage inpaintingdeep learningcomputer vision

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