华南理工大学学报（自然科学版）2025，Vol.53Issue(3)：31-39,9.DOI:10.12141/j.issn.1000-565X.240155
基于混合编码和掩膜空间调制的图像补全算法
Image Inpainting Algorithm Based on Hybrid Encoding and Mask Space Modulation
摘要
Abstract
Image inpainting refers to the process of filling in missing regions of an image with plausible content,which is one of the significant issues in the fields of computer vision and image processing research.Current research on image inpainting algorithms has made substantial progress.However,when dealing with complex images with large missing areas,existing algorithms still face challenges in generating high-quality complete images due to the lack of effective network structures to capture long-range dependencies and high-level semantic information in the images.To address the issue of large-scale missing image inpainting,this paper proposed an image inpainting algorithm based on hybrid encoding and mask spatial modulation.The aim is to expand the limited receptive field of image inpainting networks,effectively obtain global information from the visible regions of the image,and fully utilize the effective information from the visible regions.Firstly,a hybrid encoding network was used to extract local and global information features from the visible regions of the image.Then,a mask spatial modulation module dynamically adjusted the diversity in generating missing regions based on the size of the missing area.Finally,a method based on StyleGAN2 was used to generate complete images.Experimental results show that the proposed algorithm can effectively handle images with large-scale missing areas,generating high-quality images with diversity,and can be applied to data augmentation in visual saliency models.关键词
图像补全/图像增强/混合编码/掩膜空间调制Key words
image inpainting/image enhancement/hybrid encoding/mask space modulation分类
信息技术与安全科学引用本文复制引用
冼进,徐小茹,冼允廷,冼楚华..基于混合编码和掩膜空间调制的图像补全算法[J].华南理工大学学报（自然科学版）,2025,53(3):31-39,9.基金项目
广东省重点领域研发计划项目(2022B0101070001) （2022B0101070001）
华南理工大学中央高校基本科研业务费专项资金资助项目(Z2kjD2220210) Supported by the Key-Areas R&D Program of Guangdong Province(2022B0101070001) （Z2kjD2220210）
