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This research aims to enhance accuracy and intelligence in cultural relic restoration through digital protection methods.

June 4, 2026Open Computer ScienceOpen Access

Digital protection and virtual restoration of cultural relics based on multimodal data

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This research aims to enhance accuracy and intelligence in cultural relic restoration through digital protection methods.
Developed a virtual restoration approach using multimodal data including textures and structural lines.
Implemented Gated Convolutional Generative Adversarial Network (GC-GAN) for image restoration.
Conducted experiments comparing GC-GAN with traditional algorithms like Criminisi.
GC-GAN improved Structural Similarity Index Measure (SSIM) by 7.4% and Peak Signal-to-Noise Ratio (PSNR) by 30.5%.
Achieved better restoration effects and image quality with an average SSIM improvement of 0.83%.
Demonstrated strong robustness at lower missing rates with stable restoration outcomes.

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Randomized trial examines virtual restoration accuracy in cultural relics, indicating significant improvements in image quality.

Wenjie Guo (2026) studied this question.

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