{"title":"Image Super-Resolution through Pyramid Learning","authors":"Huayong He, Ze Li, Jianhong Li, Xiaocui Peng","doi":"10.1109/ICDH.2012.76","DOIUrl":null,"url":null,"abstract":"This paper presents a novel approach to single image super-resolution. We construct two pyramids: low-resolution image pyramid and the corresponding high-resolution image pyramid, then perform image segmentation and cluster the image patches according to a certain rule. We seek a sparse representation for each patch in pyramid via a corresponding dictionary. Our method aims to learn the relationship between the sparse coefficient of low-resolution image patch and that of the corresponding high-resolution image patch using support vector regression (SVR). So the final high-resolution image can be obtained via implementing the learned relationship on the input low-resolution image. Unlike the prior example-based method, our method does not require the external training image data. Also the experiment result display that our method get a better effect than the existing interpolation or example-based method.","PeriodicalId":308799,"journal":{"name":"2012 Fourth International Conference on Digital Home","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Digital Home","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDH.2012.76","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents a novel approach to single image super-resolution. We construct two pyramids: low-resolution image pyramid and the corresponding high-resolution image pyramid, then perform image segmentation and cluster the image patches according to a certain rule. We seek a sparse representation for each patch in pyramid via a corresponding dictionary. Our method aims to learn the relationship between the sparse coefficient of low-resolution image patch and that of the corresponding high-resolution image patch using support vector regression (SVR). So the final high-resolution image can be obtained via implementing the learned relationship on the input low-resolution image. Unlike the prior example-based method, our method does not require the external training image data. Also the experiment result display that our method get a better effect than the existing interpolation or example-based method.