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Updated: Nov 10, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Colour-Balanced Edge-Guided Digital Inpainting: Applications on Artworks.

Irina-Mihaela Ciortan1, Sony George1, Jon Yngve Hardeberg1

  • 1Department of Computer Science, NTNU-Norwegian University of Science and Technology, 2815 Gjøvik, Norway.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a new AI-powered virtual inpainting method for artwork restoration. The generative adversarial network accurately reconstructs missing details and colors, preserving cultural heritage digitally.

Area of Science:

  • Digital Art Restoration
  • Computer Vision
  • Artificial Intelligence

Background:

  • Virtual inpainting offers a non-destructive method for visualizing artwork hypotheses.
  • Accurate virtual reconstruction is vital for Cultural Heritage applications.
  • Physical restoration can present methodological and ethical challenges.

Purpose of the Study:

  • To develop an advanced inpainting algorithm for virtual artwork restoration.
  • To enhance the accuracy and detail of virtual reconstructions.
  • To address the need for non-invasive methods in cultural heritage preservation.

Main Methods:

  • A generative adversarial network (GAN) with two generators: one for edges and one for colors.
  • Color rebalancing using a loss function in the discretized gamut space.
Keywords:
colorizationdunhuang wall paintingsgenerative adversarial networksinpainting

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  • Simulation of lacunae and degradations (e.g., craquelure) using morphological variations of a random walk mask.

    • Main Results:

    • The proposed inpainting algorithm successfully reconstructs missing artwork details and colors.
    • The method mimics an artist's workflow: edges, color palette, then tones.
    • Restored images are visually satisfactory and quantitatively comparable to existing methods.

    Conclusions:

    • The GAN-based inpainting method provides a robust solution for virtual artwork restoration.
    • The approach effectively handles complex degradations and ensures chromatic accuracy.
    • This technique advances the digital preservation of cultural heritage assets.