As a vital carrier of China’s intangible cultural heritage, restoring damaged embroidery fabrics is essential for the sustainable preservation of cultural relics. However, existing methods face persistent challenges, such as mask pattern mismatches and restoration size constraints. To address these gaps, this study proposes an embroidery image restoration framework based on enhanced generative adversarial networks (GANs). Specifically, the framework integrates a U-Net generator with a multi-scale discriminator augmented by an attention mechanism and dual-path residual blocks to significantly enhance texture generation. Furthermore, fabric damage was classified into three categories (hole-shaped, crease-shaped, and block-shaped), with complex patterns simulated through dynamic randomization. Grid-based overlapping segmentation and pixel fusion techniques enable arbitrary-dimensional restoration. Quantitative evaluations demonstrated exceptional performance in complex texture restoration, achieving a structural similarity index (SSIM) of 0.969 and a peak signal-to-noise ratio (PSNR) of 32.182 dB. Complementarily, eye-tracking experiments revealed no persistent visual fixation clusters in the restored regions, confirming perceptual reliability. This approach establishes an efficient digital conservation pathway that promotes resource-efficient and sustainable heritage conservation.
Wang et al. (Thu,) studied this question.