Mural Inpainting Algorithm Based on Semantic Reasoning and Joint Learning
We propose a mural inpainting algorithm based on semantic inference and dynamic joint learning to address the issues of semantic inconsistency and disordered structural textures caused by the lack of semantic constraints and isolated restoration of texture structure in existing deep learning methods for mural restoration.Firstly,a mural restoration framework based on joint learning is constructed,and a joint hierarchical network is designed to divide the mural into high-level semantics and low-level semantics,enabling hierarchical restoration of different semantics.Secondly,a joint global generation module is designed to model the global semantics of the mural through autoregressive modeling and infer the repaired global semantic information.Next,a joint local generation module is constructed,which introduces a context aggregation block to learn the contextual information of the mural and generate local information for the mural.Then,a joint attention mechanism is introduced to enable collaborative training between the global semantic restoration module and the local restoration module,overcoming the issues of error accumulation and semantic inconsistency caused by isolated restoration.Finally,a joint attention mechanism is introduced to enable collaborative training between the global semantic restoration module and the local restoration module,addressing the issues of error accumulation and semantic inconsistency caused by isolated restoration.Experimental results on real Dunhuang murals show that the proposed method achieves better consistency in restoration compared to other methods,both in terms of peak signal-to-noise ratio and structural similarity.Additionally,the objective evaluation metrics surpass those of the comparison algorithms.
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