Ying Gu, Yanyun Qu, Tian-Zhu Fang, Cuihua Li, Hanzi Wang
{"title":"Image super-resolution based on multikernel regression","authors":"Ying Gu, Yanyun Qu, Tian-Zhu Fang, Cuihua Li, Hanzi Wang","doi":"10.1109/ICMLC.2012.6359503","DOIUrl":null,"url":null,"abstract":"In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid selecting kernels via a large number of cross-verifications, the multikernel regression is applied to learn the map function. This approach is efficient and the experimental results show that it manifests a high-quality performance in comparison with other superresolution methods.","PeriodicalId":331913,"journal":{"name":"Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6359503","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
In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches are promising, but kernel selection is a critical problem. In order to avoid selecting kernels via a large number of cross-verifications, the multikernel regression is applied to learn the map function. This approach is efficient and the experimental results show that it manifests a high-quality performance in comparison with other superresolution methods.