计算机技术与发展2024，Vol.34Issue(7)：9-16,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0098
多分辨率特征协作的图像修复网络
Multi-resolution Feature Collaboration Image Inpainting Network
摘要
Abstract
Deep generative methods have recently made considerable progress in the field of image inpainting by employing a coarse-to-fine strategy,but multi-stage inpainting methods with serially connected sub-networks result in discontinuous image structures and blurred details due to inaccurate structural localization and the poor feature expressiveness of the bottleneck layer.To address the above problems,a multi-resolution feature collaborative image inpainting network is proposed to inpaint damaged images with a parallel multi-resolution network structure.Parallel multi-resolution encoding is performed on the damaged image to learn the structural features at different scales,and the iterative fusion module is used to dynamically fuse the multi-scale information to provide a more accurate localization for the recovery of the damaged structure,thus generating a structurally coherent image.The gated multi-feature extraction module is used in the bottleneck layer to combine the advantages of the attention mechanism and the convolutional operation,to capture the long-distance dependencies in different dimensions and extract the features under different receptive fields,and then the gated residual fusion is used to adjust the weights of the multi-features,to enhance the feature expression ability of the bottleneck layer,so as to recover the image details of the missing regions better.Extensive experiments on the CelebA-hq dataset,the FFHQ dataset and the Paris StreetView dataset show that the proposed method provides a larger improvement in PSNR,SSIM and FID metrics and in visual quality compared to other image inpainting methods.关键词
图像修复/并行的多分辨率网络/融合机制/注意力机制/卷积操作Key words
image inpanting/parallel multi-resolution network/fusion mechanism/attention mechanism/convolutional operation分类
信息技术与安全科学引用本文复制引用
晏乙涵,吴昊,袁国武..多分辨率特征协作的图像修复网络[J].计算机技术与发展,2024,34(7):9-16,8.基金项目
国家自然科学基金(62061049,11663007) （62061049,11663007）
云南省科技厅云南大学双一流建设联合专项(202201BF070001-005) （202201BF070001-005）
