Artwork restoration using paired image translation-based generative adversarial networks

Abstract

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.

Keywords

• image translation
• generative adversarial networks
• image restoration
• deep learning
• image inpainting
• artwork restoration