Progressive Mural Inpainting Algorithm Based on Joint Kernel Prediction and Feature Reasoning

被引：0
|
作者：
Chen, Yong [1 ,2 ]
Zhao, Mengxue [1 ]
Du, Wanjun [1 ]
Tao, Meifeng [1 ]
机构：
[1] School of Electronic Engineering, Lanzhou Jiaotong University, Lanzhou
[2] Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou
来源：
Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2024年 / 51卷 / 06期
基金：
中国国家自然科学基金;
关键词：
feature fusion; image reconstruction; kernel predicting; mural inpainting; semantic feature reasoning;
D O I：
10.16339/j.cnki.hdxbzkb.2024261
中图分类号：
学科分类号：
摘要：
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. © 2024 Hunan University. All rights reserved.
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页码：1 / 9
页数：8
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