Thangka mural style transfer based on progressive style-attentional network and multi-level loss function

Style transfer is a challenging computer vision task. As an important cultural heritage in the Tibetan region, Thangka murals cover history, culture, and religion. The stylistic recreation of Thangka images not only promotes better appreciation of Thangka art but also provides a new art form for Thangka. Two challenges prevent the existing methods from solving Thangka stylization problems: (1) feature point based attention networks ignore manifold alignment of features between content images and style images and are unable to achieve regional consistency of stylized Thangka images well. (2) Due to the lack of comprehensive understanding of content and style losses in previous studies, the style-content trade-off of the stylized Thangka is unable to be guaranteed. To solve these problems, we propose a progressive style-attentional network (PSANet) and a multi-level loss function strategy for Thangka style transfer. The proposed method consists of two parts: (1) a PSANet to align content manifolds and style manifolds. (2) A multi-level loss function strategy to achieve a balance between content and style of the stylized Thangka images and to ensure better content preservation. Qualitative and quantitative experiments show that our proposed method is able to achieve satisfactory stylized effects on Thangka images. (c) 2023 SPIE and IS&T