关键词：
image processing
mural inpainting
structural and texture enhancement
dynamic convolution
multi-granularity feature extraction
摘要：
For the existing deep learning image restoration methods,the joint guidance of structure and texture information is not considered,which leads to structural disorder and texture blur in the restoration results.A generative adversarial mural inpainting algorithm based on structural and texture hybrid enhancement was ***,the structure guidance branch composed of dynamic convolution cascade was constructed to improve the expression ability of structure features,and the structure information was used to guide the encoder coding to enhance the edge contour information of the coding feature ***,the multi-granularity feature extraction module was designed to obtain the texture features of texture guided branches,and the multi-scale texture information was used to guide the decoder to reconstruct and repair,so as to improve the texture consistency of ***,skip connection was used to promote the feature sharing of structure and texture features,and the spectral-normalized PatchGAN discriminator was used to complete the mural *** digital restoration experiment results of real Dunhuang murals showed that the proposed method was better than the comparison algorithms in both subjective and objective evaluation,and the restoration results were clearer and more natural.
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中文科技期刊
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