基于增强深度扩展卷积神经网络的图像去噪方法及系统

Meng Li, Kaili Feng, Tianping Li, Guanxing Li
{"title":"基于增强深度扩展卷积神经网络的图像去噪方法及系统","authors":"Meng Li, Kaili Feng, Tianping Li, Guanxing Li","doi":"10.1109/MLISE57402.2022.00094","DOIUrl":null,"url":null,"abstract":"In order to improve the performance of image denoising, in the case of can not only reduce the computational cost at the same time to ensure the superiority of denoising, we made a change on the basis of the original network, through the way of increasing network breadth rather than depth for more features, and to improve the running speed, by means of expansion convolution to extract more information used for denoising task. A large number of experimental results show that this kind of network can not only reduce the gradient explosion, but also effectively reduce the noise intensity of the image.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image Denoising Method and System Based on Enhanced Deep Dilated Convolutional Neural Network\",\"authors\":\"Meng Li, Kaili Feng, Tianping Li, Guanxing Li\",\"doi\":\"10.1109/MLISE57402.2022.00094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the performance of image denoising, in the case of can not only reduce the computational cost at the same time to ensure the superiority of denoising, we made a change on the basis of the original network, through the way of increasing network breadth rather than depth for more features, and to improve the running speed, by means of expansion convolution to extract more information used for denoising task. A large number of experimental results show that this kind of network can not only reduce the gradient explosion, but also effectively reduce the noise intensity of the image.\",\"PeriodicalId\":350291,\"journal\":{\"name\":\"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MLISE57402.2022.00094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MLISE57402.2022.00094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了提高图像去噪的性能,在既能降低计算成本同时又能保证去噪的优越性的情况下,我们在原有网络的基础上进行了改变,通过增加网络宽度而非深度的方式获取更多的特征,并提高运行速度,通过展开卷积的方式提取更多的信息用于去噪任务。大量的实验结果表明,这种网络不仅可以减少梯度爆炸,而且可以有效地降低图像的噪声强度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Denoising Method and System Based on Enhanced Deep Dilated Convolutional Neural Network
In order to improve the performance of image denoising, in the case of can not only reduce the computational cost at the same time to ensure the superiority of denoising, we made a change on the basis of the original network, through the way of increasing network breadth rather than depth for more features, and to improve the running speed, by means of expansion convolution to extract more information used for denoising task. A large number of experimental results show that this kind of network can not only reduce the gradient explosion, but also effectively reduce the noise intensity of the image.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信