Research on Dunhuang Style Line Drawing Generation Based on Deep Learning

Dunhuang murals, as invaluable cultural heritage, hold distinct artistic significance. Transforming them into line drawings aids in preservation and creative reuse. However, current deep learning-based style transfer methods often suffer from blurred lines, style inconsistency, and poor structural fidelity. To overcome these challenges, this paper proposes DMLT-GAN, a GAN-based model integrating multi-level convolution, asymmetric cycle consistency loss, and gradient loss to enhance the stylistic fidelity and line clarity. Experiments demonstrate that DMLT-GAN achieves superior visual quality and stylistic accuracy, contributing to the digital preservation and artistic revival of Dunhuang art.