Color restoration of mural images based on a reversible neural network: leveraging reversible residual networks for structure and texture preservation

Abstract The Mogao Grottoes in Dunhuang, a treasure of China's and the world's cultural heritage, contains rich historical and cultural deposits and has left precious relics of the history of human art. Over centuries, the Mogao Caves have been affected by natural and human factors, resulting in irreversible fading and discoloration of many murals. In recent years, deep learning technology has shown great potential in the field of virtual mural color restoration. Therefore, this paper proposes a mural image color restoration method based on a reversible neural network. The method first employs an automatic reference selection module based on structural and texture similarity to choose suitable reference mural images for the faded murals. Then, it utilizes a reversible residual network to extract deep features of the mural images without information loss. Next, a channel refinement module is used to eliminate redundant information in the network channels. Finally, an unbiased color transfer module restores the color of the faded mural images. Compared to other image color restoration methods, the proposed method achieves superior color restoration effects while effectively preserving the original structure and texture details of the mural images. Compared to baseline methods, the Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Perception-based Image Quality Evaluator (PIQE) values are improved by 7.97%, 3.46%, and 13.98%, respectively. The color restoration of the Dunhuang Mural holds significant historical, artistic, cultural, and economic values, and plays a positive role in the preservation and inheritance of Chinese culture, as well as in the promotion of cultural exchange and mutual understanding.