{"title":"Image Super-Resolution with Deep Convolutional Neural Network","authors":"Xiancai Ji, Yao Lu, Li Guo","doi":"10.1109/DSC.2016.104","DOIUrl":null,"url":null,"abstract":"We present a computational model for image super-resolution. Apart from using deep Convolutional Neural Network to map between the low-resolution images and high-resolution images, we adopt stepwise refinement method to improve the reconstruction results and introduce the back projection algorithm to avoid diffusion of error pixels.","PeriodicalId":295898,"journal":{"name":"2016 IEEE First International Conference on Data Science in Cyberspace (DSC)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE First International Conference on Data Science in Cyberspace (DSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSC.2016.104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We present a computational model for image super-resolution. Apart from using deep Convolutional Neural Network to map between the low-resolution images and high-resolution images, we adopt stepwise refinement method to improve the reconstruction results and introduce the back projection algorithm to avoid diffusion of error pixels.
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