A visual design method for Dunhuang murals based on deep learning and graphical algorithms

Dunhuang murals in the Mogao Grottoes are regarded as one of the most significant representations of the visual culture of the Buddhists, though old age, contact with the environment, and loss of color values the murals are under a threat of being conserved. Digital restoration is provided in the form of addition to the traditional conservation, yet the existing practices fail to consider cultural and compositional values peculiar to Dunhuang art. The framework of a deep learning, GECST (GAN-Enhanced Culture Style Transfer), is presented which consists of three stages, preprocessing phase, multi-scale features encoding, GAN-based generation, and refinement to create visual realistic outputs that are culturally consistent. The GECST assessment offers experimental data that the quality of the manufactured restorations is high and that it preserves the critical artistic and cultural aspects, and survives compared to the traditional GAN-based models in qualitative research. Interpretations The recovered images can be put in digital archival use, teaching, as well as conservation planning, and artistic use, which provide a workable method of using and passing the heritage of Dunhuang mural. The cultural sensitivity and technical accuracy will assist GECST to bridge the gap between the automated digital restoration and the demands of the historically aware heritage conservation, and offer a scalable and culturally sensitive answer to the preservation of digital heritage.