Preserving Artistic Heritage: A Comprehensive Review of Virtual Restoration Methods for Damaged Artworks

IF 9.7 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Praveen Kumar, Varun Gupta
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引用次数: 0

Abstract

Restoration of damaged artwork is an important task to preserve the culture and history of humankind. Restoration of damaged artwork is a delicate, complex, and irreversible process that requires preserving the artist’s style and semantics while removing the damages from the artwork. Digital restoration of artworks can guide artists in physically restoring artworks. This paper groups the virtual artwork restoration methods into various categories: image processing, machine learning, encoder-decoder neural networks, and generative adversarial network-based methods. This paper discusses and analyses different restoration methods’ underlying merits and demerits. The category-wise review of various artwork restoration methods reveals that the generative adversarial network-based methods have attracted the attention of researchers in recent years for restoring damaged artworks. This paper describes datasets used for training and testing of artwork restoration methods and discusses various metrics used for performance evaluation of the artwork restoration methods. This paper compares the restoration results of various methods quantitatively using performance evaluation metrics and qualitatively using visual inspection of the results. Further, the paper also identifies research gaps, challenges, and future directions for research in this field. The proposed review aims to provide researchers with an important reference for working in the artwork restoration field.

Abstract Image

保护艺术遗产:全面评述受损艺术品的虚拟修复方法
修复受损艺术品是保护人类文化和历史的一项重要任务。受损艺术品的修复是一个微妙、复杂和不可逆的过程,需要在清除艺术品上的损伤的同时保留艺术家的风格和语义。艺术品的数字化修复可以指导艺术家对艺术品进行物理修复。本文将虚拟艺术品修复方法分为不同类别:图像处理、机器学习、编码器-解码器神经网络和基于生成对抗网络的方法。本文讨论并分析了不同修复方法的基本优缺点。通过对各种艺术品修复方法进行分类审查,发现基于生成式对抗网络的方法近年来在修复受损艺术品方面吸引了研究人员的关注。本文介绍了用于训练和测试艺术品修复方法的数据集,并讨论了用于艺术品修复方法性能评估的各种指标。本文使用性能评估指标对各种方法的修复结果进行了定量比较,并使用目测结果对各种方法的修复结果进行了定性比较。此外,本文还指出了该领域的研究空白、挑战和未来研究方向。本综述旨在为艺术品修复领域的研究人员提供重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
19.80
自引率
4.10%
发文量
153
审稿时长
>12 weeks
期刊介绍: Archives of Computational Methods in Engineering Aim and Scope: Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication. Review Format: Reviews published in the journal offer: A survey of current literature Critical exposition of topics in their full complexity By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.
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