{"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}
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.