Textiles belong to organic substances,with relatively poor chemical stability.Textile relics often suffer from issues such as fading,incompleteness,breakage,and contamination after excavation,greatly affecting the meticulous study of ancient costumes.Traditional textile restoration methods mostly rely on manual intervention,which is time-consuming and laborious,with restoration results varying from person to person.Therefore,the article simulates damaged excavated garments using obscured clothing images and employs Generative Adversarial Networks for restoration.It analyzes the restoration effects of Generative Adversarial Networks,establishes a dataset of ancient clothing,and provides new perspectives and avenues for future research in garment restoration.
维普
万方数据