{"title":"利用深度学习实时恢复移动图像中的高质量失真","authors":"T. Koçak, Cagkan Ciloglu","doi":"10.1109/AIKE48582.2020.00025","DOIUrl":null,"url":null,"abstract":"Frames provided by camera on mobile devices may be distorted because of camera defects and/or weather conditions such as rain and snow. These distortions affect image classifiers. This paper proposes using deep-learning architectures to restore quality distortions in real-time mobile video for image classifiers. An iOS based app is developed using CoreML to show that deep convolutional auto-encoder (CAE) based methods can be used to restore picture quality.","PeriodicalId":370671,"journal":{"name":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time Restoration of Quality Distortions in Mobile Images using Deep Learning\",\"authors\":\"T. Koçak, Cagkan Ciloglu\",\"doi\":\"10.1109/AIKE48582.2020.00025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Frames provided by camera on mobile devices may be distorted because of camera defects and/or weather conditions such as rain and snow. These distortions affect image classifiers. This paper proposes using deep-learning architectures to restore quality distortions in real-time mobile video for image classifiers. An iOS based app is developed using CoreML to show that deep convolutional auto-encoder (CAE) based methods can be used to restore picture quality.\",\"PeriodicalId\":370671,\"journal\":{\"name\":\"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIKE48582.2020.00025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIKE48582.2020.00025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time Restoration of Quality Distortions in Mobile Images using Deep Learning
Frames provided by camera on mobile devices may be distorted because of camera defects and/or weather conditions such as rain and snow. These distortions affect image classifiers. This paper proposes using deep-learning architectures to restore quality distortions in real-time mobile video for image classifiers. An iOS based app is developed using CoreML to show that deep convolutional auto-encoder (CAE) based methods can be used to restore picture quality.
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