[1]杨记鑫,胡伟霞,赵 杰,等.基于生成对抗网络的图像超分辨算法[J].计算机技术与发展,2022,32(04):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 010]
　YANG Ji-xin,HU Wei-xia,ZHAO Jie,et al.Image Super Resolution Algorithm Based on Generative Countermeasure Network[J].,2022,32(04):57-62.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 010]
点击复制

基于生成对抗网络的图像超分辨算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年04期
页码:
57-62
栏目:
图形与图像
出版日期:
2022-04-10

文章信息/Info

Title:
Image Super Resolution Algorithm Based on Generative Countermeasure Network
文章编号:
1673-629X(2022)04-0057-06
作者:
杨记鑫1; 2 ; 胡伟霞1; 2 ; 赵 杰1; 2 ; 徐灵飞1; 2
1. 成都理工大学 工程技术学院,四川 乐山 614000;
2. 核工业西南物理研究院,四川 成都 610225
Author(s):
YANG Ji-xin1; 2 ; HU Wei-xia1; 2 ; ZHAO Jie1; 2 ; XU Ling-fei1; 2
1. Engineering and Technical College of Chengdu University of Technology,Leshan 614000,China;
2. Southwesten Institute of Physics for Nuclear Industry,Chengdu 610225,China
关键词:
生成对抗网络; 超分辨; 图像处理; 深度学习; 卷积
Keywords:
generative adversarial networks; super-resolution; image processing; deep learning; convolution
分类号:
TP183;TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 04. 010
摘要:
图像超分辨是使低分辨率图像通过端到端训练产生边缘更清晰的高分辨率图像的一种技术,是数字图像处理的一个重要研究方向。 该文提出了一种基于生成对抗网络的图像超分辨算法,并对网络结构进行改进。 设计的生成器删除了残差块的 BN 层,增加了特征识别的相关算法,特征提取部分采用两层卷积网络,可以提取更多的图像特征,在低分辨率图像上提取特征,通过卷积计算得到高分辨率图像,可以提升运算结果的准确性。 判别器设计采用先分组再整合的思想,将生成图像划分成一定数量的图像块,计算每一部分的判别结果,然后将所有图像块的判别真假组合起来,作为最终的判别结果。 经实验验证,设计的网络模型在图像重建效果上有了一定的提高,并节省了一定的运算时间。
Abstract:
Image super-resolution is a technology to make low resolution image produce high-resolution image with clearer edge through end-to-end training,which is an significant study orientation of digital image processing. An image super-resolution algorithm based on generative countermeasure network is proposed,and the network structure is improved. The BN layer of residual block is deleted,and the related algorithm of feature recognition is added. The feature extraction part adopts two - layer convolution network,which can extract more image features,extract features on low resolution image,and get high-resolution image through convolution calculation,which can improve the accuracy of operation results. The design of the discriminator adopts the idea of grouping first and then integrating. The whole image is divided into several small image blocks,and the discriminating results of each image block are calculated respectively.Finally,the true and? false discriminating results of all image blocks are combined as the final discriminating results. The experimental results show that the designed network model has a certain improvement in image reconstruction effect,and saves a certain amount of computing time.

相似文献/References:

[1]康嘉钰,苏凡军.基于生成对抗网络的长短兴趣推荐模型[J].计算机技术与发展,2020,30(06):35.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 007]
　KANG Jia-yu,SU Fan-jun.A Long-short-term Interests Recommendation Model Based on Generative Adversarial Networks[J].,2020,30(04):35.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 007]
[2]蒋文杰,罗晓曙*,戴沁璇.一种改进的生成对抗网络的图像上色方法研究[J].计算机技术与发展,2020,30(07):56.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 013]
　JIANG Wen-jie,LUO Xiao-shu*,DAI Qin-xuan.Research on an Improved Method of Generative Adversarial Networks Image Coloring[J].,2020,30(04):56.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 013]
[3]周传华,吴幸运,李 鸣.基于 WGAN 单帧人脸图像超分辨率算法[J].计算机技术与发展,2020,30(09):29.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 006]
　ZHOU Chuan-hua,WU Xing-yun,LI Ming.Single Frame Face Images Super-resolution Algorithm Based on WGAN[J].,2020,30(04):29.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 006]
[4]庄兴旺,丁岳伟.多维度注意力和语义再生的文本生成图像模型[J].计算机技术与发展,2020,30(12):27.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 005]
　ZHUANG Xing-wang,DING Yue-wei.Text-to-image Model by Multidimensional Attention and Semantic Regeneration[J].,2020,30(04):27.[doi:10. 3969 / j. issn. 1673-629X. 2020. 12. 005]
[5]尹玉婷,肖秦琨.基于深度卷积生成对抗网络的图像生成[J].计算机技术与发展,2021,31(04):86.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 015]
　YIN Yu-ting,XIAO Qin-kun.Image Generation Based on Deep Convolution GenerativeAdversarial Networks[J].,2021,31(04):86.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 015]
[6]程换新,张志浩,刘文翰,等.基于生成对抗网络的图像识别[J].计算机技术与发展,2021,31(06):175.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 031]
　CHENG Huan-xin,ZHANG Zhi-hao,LIU Wen-han,et al.Image Recognition Based on Generative Adversarial Network[J].,2021,31(04):175.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 031]
[7]苑金辉,乔 艳,费烨琳,等.基于深度迁移学习的心脏 MRI 图像左心室分割[J].计算机技术与发展,2021,31(06):35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 007]
　YUAN Jin-hui,QIAO Yan,FEI Ye-lin,et al.Left Ventricular Segmentation in Cardiac MRI Images Based onDeep Transfer Learning[J].,2021,31(04):35.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 007]
[8]王田丰,胡谷雨,王 睿,等.基于 AAE 的网络性能异常发现[J].计算机技术与发展,2021,31(07):113.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 019]
　WANG Tian-feng,HU Gu-yu,WANG Rui,et al.AAE-based Anomaly Detection for Network Performance[J].,2021,31(04):113.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 019]
[9]徐志鹏,卢官明,罗燕晴.基于 CycleGAN 的人脸素描图像生成[J].计算机技术与发展,2021,31(08):63.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 011]
　XU Zhi-peng,LU Guan-ming,LUO Yan-qing.Face Sketch Image Generation Based on CycleGAN[J].,2021,31(04):63.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 011]
[10]王荣达,刘宁钟,李强懿,等.一种基于生成对抗网络的轻量级图像翻译模型[J].计算机技术与发展,2021,31(11):52.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 009]
　WANG Rong-da,LIU Ning-zhong,LI Qiang-yi,et al.A Lightweight Image-to-image Translation Model Based on GAN[J].,2021,31(04):52.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 009]

更新日期/Last Update: 2022-04-10