Discrete codebook collaborating with transformer for thangka image inpainting

Thangka, as a precious heritage of painting art, holds irreplaceable research value due to its richness in Tibetan history, religious beliefs, and folk culture. However, it is susceptible to partial damage and form distortion due to natural erosion or inadequate conservation measures. Given the complexity of textures and rich semantics in thangka images, existing image inpainting methods struggle to recover their original artistic style and intricate details. In this paper, we propose a novel approach combining discrete codebook learning with a transformer for image inpainting, tailored specifically for thangka images. In the codebook learning stage, we design an improved network framework based on vector quantization (VQ) codebooks to discretely encode intermediate features of input images, yielding a context-rich discrete codebook. The second phase introduces a parallel transformer module based on a cross-shaped window, which efficiently predicts the index combinations for missing regions under limited computational cost. Furthermore, we devise a multi-scale feature guidance module that progressively fuses features from intact areas with textural features from the codebook, thereby enhancing the preservation of local details in non-damaged regions. We validate the efficacy of our method through qualitative and quantitative experiments on datasets including Celeba-HQ, Places2, and a custom thangka dataset. Experimental results demonstrate that compared to previous methods, our approach successfully reconstructs images with more complete structural information and clearer textural details.