Mural restoration algorithm based on improved Swin Transformer
Addressing the damage caused to murals by natural factors and human destruction,traditional image restoration methods fail to capture the advanced semantic information of the images,while conventional deep learning-based methods often re-sult in blurred edges and poor restoration effects for large-scale damages.To more reasonably restore the original appearance of damaged murals,a context encoder-decoder is designed by leveraging the local feature extraction capability of traditional convolu-tions and the global structural understanding of the Swin Transformer.Ultimately,a two-stage generative adversarial network based on Swin Transformer is constructed.Experimental results show that,the restoration effect outperforms current mainstream algo-rithms both subjectively and objectively.
cultural relic protectionimage restorationSwin Transformer
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