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This chapter introduces a suite of digital tools and systems that were developed within the PERCEIVE project to address challenges related to the conservation and communication of colour in cultural heritage collections. The authors, who developed these tools, describe the context and motivation behind, as well as their key features and functionalities. They also present demo use cases to showcase the performance of the tools. A key focus of the PERCEIVE project was the development of the Colour Knowledge Repository, which adheres to the FAIR principles for data sharing and reuse. The PERCEIVE platform offers the CH community centralised access to a variety of colour analysis tools, encouraging broader adoption and future integration. Tools such as MuLaX allow users to visualise and interact with information about the materials, techniques and preservation state of objects, enabling them to meaningfully discover scientific information. StyleShade3D assists experts in developing digital reconstructions of 3D objects, enabling them to apply different styles and shades to various parts of a 3D model using machine learning methods. The Light Damage Estimator provides digital simulations and visualisations to predict colour changes in photosensitive materials exposed to light. The aim is to facilitate informed decision-making regarding exhibition lighting and long-term preservation strategies. Two tools developed in PERCEIVE focus specifically on autochromes. Deep Degreening uses AI restoration models based on networks that have been trained using synthetic image datasets simulating genuine greening defects to remove such defects from digitised autochromes. The Text2Autochromes uses generative AI to produce autochrome images. Finally, The Lenticular Colour Reconstruction Tool allows to recover the original colour information embedded in early lenticular films such as Kodacolor. All tools are now fully functional and have undergone testing to ensure stability and consistent performance.

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Tools and Services for Colour Change Analysis and Sharing of Knowledge

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Irina-Mihaela Ciortan,
  • Bruno Fanini,
  • Paul Julius Kühn,
  • Marcello Massidda,
  • Paschalis Panteleris,
  • George Papadopoulos,
  • Marios Pitikakis,
  • Saptarshi Neil Sinha,
  • Panagiotis Siozos,
  • Sophia Sotiropoulou,
  • Petros Ioannis Stavroulakis,
  • Giorgio Trumpy

摘要

This chapter introduces a suite of digital tools and systems that were developed within the PERCEIVE project to address challenges related to the conservation and communication of colour in cultural heritage collections. The authors, who developed these tools, describe the context and motivation behind, as well as their key features and functionalities. They also present demo use cases to showcase the performance of the tools. A key focus of the PERCEIVE project was the development of the Colour Knowledge Repository, which adheres to the FAIR principles for data sharing and reuse. The PERCEIVE platform offers the CH community centralised access to a variety of colour analysis tools, encouraging broader adoption and future integration. Tools such as MuLaX allow users to visualise and interact with information about the materials, techniques and preservation state of objects, enabling them to meaningfully discover scientific information. StyleShade3D assists experts in developing digital reconstructions of 3D objects, enabling them to apply different styles and shades to various parts of a 3D model using machine learning methods. The Light Damage Estimator provides digital simulations and visualisations to predict colour changes in photosensitive materials exposed to light. The aim is to facilitate informed decision-making regarding exhibition lighting and long-term preservation strategies. Two tools developed in PERCEIVE focus specifically on autochromes. Deep Degreening uses AI restoration models based on networks that have been trained using synthetic image datasets simulating genuine greening defects to remove such defects from digitised autochromes. The Text2Autochromes uses generative AI to produce autochrome images. Finally, The Lenticular Colour Reconstruction Tool allows to recover the original colour information embedded in early lenticular films such as Kodacolor. All tools are now fully functional and have undergone testing to ensure stability and consistent performance.