联合核预测和特征推理的渐进式壁画修复算法OA北大核心CSTPCD

Aiming at the existing depth model that fails to take into account both pixel-level features and semantic-level features at the same time when repairing nurals,resulting in problems such as lack of texture fineness and structural distortion,a progressive mural inpaining algorithm that combines kernel prediction and feature reasoning is proposed.Firstly,the regional progressive module is designed to realize the progressive mapping of mural features through partial convolution.Then,a dual-branch repair module is proposed,in which the kernel predicts the volume integral branch to realize the pixel-level repair of the damaged area.The semantic feature reasoning branch introduces gated deformable convolution and combines the semantic consistency attention mechanism to realize feature reasoning to complete the semantic-level repair of damaged murals.Finally,the two-branch repair results are fused into the output to minimize the reconstruction error and improve the repair accuracy.Through the digital restoration experiment of Dunhuang murals,the results show that the restored murals by the proposed method have better structural texture characteristics,which are better than the comparison algorithm in terms of evaluation indicators.