ARTICT | Art Through the ICT Lens: Big Data Processing Tools to Support the Technical Study, Preservation and Conservation of Old Master Paintings

艺术 |

• 批准号: EP/R032785/1
• 负责人: Miguel Rodrigues
• 金额: $ 96.75万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR032785%2F1
• 关键词: ARTICT; Art; Through; ICT; Lens

The heritage science sector is experiencing a digital revolution linked to the emergence and increasing adoption of cutting-edge non-invasive analytical imaging techniques generating large volumes of multidimensional data from cultural heritage objects. These include macro X-Ray fluorescence (MA-XRF) scanning and hyperspectral imaging (HSI). Combining MA-XRF and HSI data - providing elemental and molecular information - offers huge potential for improved identification, characterization, and visualisation of the materials and features of interest in a painting, including in sub-surface layers of the painting. However, it is increasingly recognised that the wealth of data associated with these modalities cannot be fully exploited through traditional primarily manual approaches to interrogating heritage science data.The aim of this research - bringing together ICT and Heritage Science researchers to enable the cross-pollination of ideas and expertise - is to co-create new automatic signal analysis and processing tools that are able to 'fuse' MA-XRF and HSI data to support the technical study, conservation and preservation artwork.In particular, the proposed tools will provide the means to identify, characterize and visualize materials present within a painting, thereby leading to new insights relevant for the conservation and preservation of Old Master paintings or to new ways to engage the public with cultural heritage, science and ICT. For example, the proposed tools will also help visualise in a more integrated and accessible form the features of interest for art-historical study and conservation, such as underdrawing, pentimenti, concealed designs, losses or non-original materials.To develop such tools, an ambitious research programme is proposed that includes development of: 1) multidimensional multimodal heritage science datasets, 2) multimodal signal processing algorithms for data correction, alignment, registration and mosaicking; and 3) new multimodal signal analysis algorithms capable of inferring material distributions in a painting from MA-XRF and HSI data. In addition, this research programme also envisions a number of significant advances in the area of signal processing - including new sparsity-driven nonlinear unmixing algorithms - that address the specific challenges arising in art investigation that do not arise in other application domains of signal processing, such as the need to identity mixtures of (aged) materials in superimposed layers using combined MA-XRF and HSI datasets.The research programme also includes a number of case studies on National Gallery paintings that will assess the validity, potential and relevance of the proposed tools to the wider heritage sector.The other aim of this research is to champion and sustain co-creation activities across the ICT and Heritage Science sectors. The activities planned during and beyond the project include: a) training of researchers, doctoral students, and undergraduate students; b) dedicated courses, workshops and events; c) outreach and public engagement activities; and d) a UK-wide network in the area of "ICT for Art Investigation". These aim to augment the project's co-creation and cross-disciplinary ethos and catalyse further research.The ideas and tools conceived throughout the research will lead to impact across various levels and communities: a) the ICT sector will be exposed to new challenges stimulating developments in a new area of signal processing for art investigation; b) the Heritage Science sector will benefit from new automated, accessible, robust, user-friendly tools to aid the work of heritage scientists, art historians, and conservators. Finally, the tools and the images and insights they create will provide galleries with new innovative means to interpret and present their collections to the general public.

遗产科学部门正在经历一场数字革命，这与尖端的非侵入性分析成像技术的出现和日益普及有关，这些技术可以从文物中产生大量多维数据。这些包括宏观x射线荧光（MA-XRF）扫描和高光谱成像（HSI）。结合MA-XRF和HSI数据-提供元素和分子信息-为改进绘画中感兴趣的材料和特征的识别，表征和可视化提供了巨大的潜力，包括绘画的次表层。然而，人们越来越认识到，与这些模式相关的大量数据不能通过传统的主要手工方法来充分利用，以询问遗产科学数据。这项研究的目的是将信息通信技术和遗产科学研究人员聚集在一起，实现思想和专业知识的交叉交流，共同创造新的自动信号分析和处理工具，能够“融合”MA-XRF和HSI数据，以支持技术研究、保护和保存艺术品。特别是，所提出的工具将提供识别、表征和可视化绘画中存在的材料的手段，从而为保护和保存早期大师画作带来新的见解，或为公众参与文化遗产、科学和信息通信技术提供新的途径。例如，拟议的工具还将有助于以更综合和可访问的形式将艺术史研究和保护感兴趣的特征可视化，例如底画，笔画，隐藏设计，损失或非原始材料。为了开发这样的工具，提出了一个雄心勃勃的研究计划，包括开发：1)多维多模态遗产科学数据集；2)用于数据校正、对准、配准和拼接的多模态信号处理算法；3)新的多模态信号分析算法，能够从MA-XRF和HSI数据推断绘画中的材料分布。此外，该研究计划还设想了信号处理领域的一些重大进展-包括新的稀疏驱动的非线性解混算法-解决了在信号处理的其他应用领域中没有出现的艺术调查中出现的特定挑战，例如需要使用MA-XRF和HSI数据集来识别叠加层中（老化）材料的混合物。该研究计划还包括对国家美术馆画作的一些案例研究，这些案例将评估所提议的工具对更广泛的遗产部门的有效性、潜力和相关性。这项研究的另一个目的是支持和维持ICT和遗产科学部门的共同创造活动。项目期间和项目结束后的计划活动包括：a)研究人员、博士生和本科生的培训；B)专门的课程、研讨会和活动；C)外联和公众参与活动；d)在“艺术调查信息和通信技术”领域建立一个全英国范围的网络。这些旨在增强项目的共同创造和跨学科精神，并促进进一步的研究。在整个研究过程中构思的想法和工具将对各个层面和社区产生影响：a)信息通信技术部门将面临新的挑战，刺激艺术调查信号处理新领域的发展；b)遗产科学部门将受益于新的自动化，可访问的，强大的，用户友好的工具，以帮助遗产科学家，艺术史学家和保护人员的工作。最后，他们创造的工具、图像和见解将为画廊提供新的创新手段来解释和向公众展示他们的藏品。

项目成果

Image Separation With Side Information: A Connected Auto-Encoders Based Approach.

带有侧面信息的图像分离：基于连接自动编码器的方法。

• DOI: 10.1109/tip.2023.3275872
• 发表时间: 2023
• 期刊: a publication of the IEEE Signal Processing Society
• 作者: Pu W

A connected auto-encoders based approach for image separation with side information: with applications to art investigation

基于连接自动编码器的带有辅助信息的图像分离方法：在艺术调查中的应用

• 发表时间: 2020
• 作者: Pu W

A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs

基于学习的方法来分离与隐藏设计艺术品相关的混合 X 射线图像

• DOI: 10.23919/eusipco54536.2021.9616096
• 发表时间: 2021
• 作者: Pu W

A Case of Collaboration: The Adoration of the Kings by Botticelli and Filippino Lippi Part II: Investigating the Collaboration

合作案例：波提切利和菲利皮诺·里皮的《国王崇拜》第二部分：合作调查

• 发表时间: 2020
• 期刊: National Gallery Technical Bulletin
• 作者: Jill Dunkerton

Mixed X-Ray Image Separation for Artworks With Concealed Designs.

具有隐藏设计的艺术品的混合 X 射线图像分离。

• DOI: 10.1109/tip.2022.3185488
• 发表时间: 2022
• 期刊: a publication of the IEEE Signal Processing Society
• 作者: Pu W