ArticlePloS one2025
Thangka super-resolution diffusion model based on discrete cosine transform domain padding upsampling and high-frequency focused attention.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Thangka is a traditional Tibetan painting art form, possessing profound cultural significance and a unique artistic style. Image super-resolution technology, as an effective means of digital preservation and restoration, plays an important role in maintaining the integrity and heritage of Thangka art. However, existing image super-resolution methods cannot be used for Thangka images due to the following reasons: (1) Thangka images are large in size and rich in content, making it difficult for existing models to restore the original textures of degraded Thangka images. (2) Thangka images have intricate textures, so the high-resolution Thangka images reconstructed by existing methods, which perform well on objective metrics, perform poorly in terms of human visual perception. To overcome these challenges, a Frequency-Domain Enhanced Diffusion Super-Resolution (FDEDiff) is proposed, consisting of three parts: (1) a High-Frequency Focused Cross Attention Mechanism (HFC-Attention), which can separate high-frequency features of images to guide the attention mechanism, improving the reconstruction quality of high-frequency details in the diffusion model; (2) a DCT Domain Padding Upsampling (DCT-Upsampling), which performs upsampling in the Discrete Cosine Transform (DCT) domain and improves the reconstruction of dense line areas in Thangka images by fully utilizing global information; (3) for the first time, we construct a Thangka image super-resolution dataset, which contains 82,688 pairs of 512 × 512 images. Qualitative and quantitative experiments demonstrate that the proposed method achieves state-of-the-art performance on the Thangka dataset, attaining a LPIPS score of 0.0815 (lower indicates better perceptual quality) and showing a 20% improvement in perceptual quality over baseline methods. Although the proposed method requires longer inference time due to the iterative nature of diffusion models, this computational trade-off is justified by the critical need for artistic authenticity in cultural preservation applications. Dataset is available at https://github.com/cvlabdatasets/ThangkaDatasets.
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