Restoration of damaged artworks based on a generative adversarial network

Kumar, Praveen; Gupta, Varun

doi:10.1007/s11042-023-15222-2

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…J. Li et al [23] proposed digital restoration of ancient murals using U-Net based generative adversarial network. More recently, P. Kumar and V. Gupta [24] proposed the method of restoration of damaged artworks based on generative adversarial networks. Due to the limitations of the proposed methods proposed for image and artwork restoration, there is a requirement for a better architecture of the generative adversarial network for restoring the damaged artworks.…”

Section: Artwork Restorationmentioning

confidence: 99%

Artwork restoration using paired image translation-based generative adversarial networks

Kumar

Gupta

2023

ITM Web Conf.

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Preservation of the artworks has historical and cultural importance. However, with time, environmental factors severely affect artworks, and these damages are often complicated to repair manually and through traditional methods. We propose a method to restore artwork that has been damaged over time. This work proposes a systematic approach using paired image-to-image translation based on a generative adversarial network. The experimental results have been quantitatively evaluated. The experimental results obtained from the presented work visually prove that the presented approach of artwork restoration completely restores the damaged artwork.

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Section: Artwork Restorationmentioning

confidence: 99%

Artwork restoration using paired image translation-based generative adversarial networks

Kumar

Gupta

2023

ITM Web Conf.

Self Cite

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Unpaired Image-to-Image Translation Based Artwork Restoration Using Generative Adversarial Networks

Kumar,

Gupta

2023

Smart Innovation, Systems and Technologies

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A Deep Learning Approach for Digital Color Reconstruction of Van Gogh’s Paintings Using Unpaired Areas Under the Frame

Parvathaneni,

Pisarenco,

Onvlee

2024

SN COMPUT. SCI.

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Factors such as dirt accumulation, aging of paint materials and chemical reactions with the surrounding atmosphere lead to alteration and degradation of paintings. As a result, colors in the paintings can change. Van Gogh’s paintings are no exception to this process, and virtual reconstruction of the original colors is a very challenging problem. In this work we propose a novel approach for color reconstruction that does not require any pre-existing digital reconstructions, physical reproductions or artificial aging experiments, and relies purely on the data within the painting. We exploit the fact that areas of the painting located under the frame are typically well-preserved (mainly due to the protection from light exposure and dirt accumulation offered by the frame) and contain colors which are relatively close to their original look. Inspired by the recent advances in machine learning techniques for unpaired image-to-image translation, a practical weakly supervised approach for digital color reconstruction is formulated. Moreover, its performance is demonstrated for paintings by Vincent van Gogh. To our knowledge, this is the first time that a method for color reconstruction that relies purely on the data available within one painting is described in literature.

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Restoration of damaged artworks based on a generative adversarial network

Cited by 5 publications

References 34 publications

Artwork restoration using paired image translation-based generative adversarial networks

Artwork restoration using paired image translation-based generative adversarial networks

Unpaired Image-to-Image Translation Based Artwork Restoration Using Generative Adversarial Networks

A Deep Learning Approach for Digital Color Reconstruction of Van Gogh’s Paintings Using Unpaired Areas Under the Frame

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