Thangka murals are important cultural heritages of Tibet, but most of the existing Thangka images are of low resolution. Thangka mural super-resolution reconstruction is very important for the protection of Tibetan cultural heritage. Transformer-based methods have shown impressive performance in Thangka image super-resolution. However, current Transformer-based methods still face two major challenges when addressing the super-resolution problem of Thangka images: (1) The self-attention mechanism used in Transformer models does not have high sensitivity to slender and curved edge textures; (2) The sliding window mechanism used in Transformer models cannot fully achieve cross-window information interaction. To resolve these problems, we propose a Thanka mural super-resolution reconstruction method based on Nimble Convolution (NConv) and Overlapping Window Self-Attention (OWSA). The proposed method consists of three parts: (1) a Nimble Convolution (NConv) to enhance the perception of slender and curved geometric structures; (2) an Overlapping Window Self-Attention (OWSA) to facilitate a more direct feature interaction among adjacent windows within the Transformer. (3) for the first time, we construct a Thangka image super-resolution dataset, which contains 8,868 pairs of \(512\times 512\) images. We expect this dataset to serve as a valuable reference for future research. Both objective and subjective evaluations validated the competitive performance of our method.