图像修复,自注意力,生成对抗网络,卷积神经网络," /> 图像修复,自注意力,生成对抗网络,卷积神经网络,"/> imageinpainting,self-attention,generative ,adversarial networks,convolutional neural networks,"/> <p class="MsoNormal"> 基于条件约束下的自注意力生成对抗网络的图像修复
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沈阳化工大学学报, 2024, 38(1): 90-96    doi: 10.3969/j.issn.2095-2198.2024.01.013
  信息与计算机工程 本期目录 | 过刊浏览 | 高级检索 |

基于条件约束下的自注意力生成对抗网络的图像修复

1.沈阳化工大学 计算机科学与技术学院, 辽宁 沈阳 110142;2.沈阳化工大学 信息工程学院, 辽宁 沈阳 110142)

Image Inpainting Based on Self-Attention Generative Adversarial Networks Under Condition Constraint

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摘要 

图像修复是图像处理领域的重要研究方向.为了解决现阶段图像修复算法存在修复区域与周围区域不一致的模糊纹理问题,提出了一种图像修复方法.该方法在生成对抗网络的基础上,引入条件特征和自注意模块,并将数据的具体维度与语义特征相关联.采用该方法训练的修复模型可以对特定类型的图像进行修复,并且保证了整体修复的一致性和局部信息细节的合理性.实验在CeleBA人脸数据集上进行训练测试,获得了良好的修复结果.

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关键词:  图像修复')" href="#">

图像修复  自注意力  生成对抗网络  卷积神经网络    

Abstract: 

Image inpainting is an important research direction in the field of image processing.In order to solve the problem of fuzzy texture in the current image inpainting algorithm that the restoration area is inconsistent with the surrounding area,an image inpainting method is proposed.Based on the generative adversarial networks,this method introduces conditional features and self-attention modules,and associates the specific dimensions of data with semantic features.The inpainting model trained by this method can inpainting specific types of images and ensure the consistency of the overall inpainting and the rationality of local information details.Experimental results show that good results can be obtained by training and testing on CeleBA face datasets.

Key words:  imageinpainting')" href="#">

imageinpainting    self-attention    generative     adversarial networks    convolutional neural networks

               出版日期:  2024-02-29      发布日期:  2024-09-24      整期出版日期:  2024-02-29
ZTFLH: 

TP751.1

 
通讯作者:  袁德成   
作者简介:  宁泽惺(1996—),男,江西抚州人,硕士研究生在读,主要从事深度学习、图像处理的研究.
引用本文:    
宁泽惺1, 袁德成2.

基于条件约束下的自注意力生成对抗网络的图像修复 [J]. 沈阳化工大学学报, 2024, 38(1): 90-96.
NING Zexing, YUAN Decheng.

Image Inpainting Based on Self-Attention Generative Adversarial Networks Under Condition Constraint . Journal of Shenyang University of Chemical Technology, 2024, 38(1): 90-96.

链接本文:  
https://xuebao.syuct.edu.cn/CN/10.3969/j.issn.2095-2198.2024.01.013  或          https://xuebao.syuct.edu.cn/CN/Y2024/V38/I1/90

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