{"title":"A Wavelet-based Progressive Super-Resolution Model","authors":"Junyi He, Hongyang Zhou, Yan Ma","doi":"10.1109/CoST57098.2022.00082","DOIUrl":null,"url":null,"abstract":"Deep-learning based Super-Resolution (SR) methods have achieved remarkable performance in many aspects. However, most existing methods are constructed for a single specific scale, and few are multi-scale, lightweight, and have a short running time. To this end, we propose a wavelet-based progressive super-resolution method (WPSR). WPSR could process low and high-frequency information separately by reconstructing different wavelet coefficients in order and could generate multiple SR images at multiple scales sequentially. Experiments demonstrate that our WPSR could outperform existing state-of-the-art methods at multiple scales, especially for ×8 and ×16 scale factors.","PeriodicalId":135595,"journal":{"name":"2022 International Conference on Culture-Oriented Science and Technology (CoST)","volume":"10 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 Culture-Oriented Science and Technology (CoST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoST57098.2022.00082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Deep-learning based Super-Resolution (SR) methods have achieved remarkable performance in many aspects. However, most existing methods are constructed for a single specific scale, and few are multi-scale, lightweight, and have a short running time. To this end, we propose a wavelet-based progressive super-resolution method (WPSR). WPSR could process low and high-frequency information separately by reconstructing different wavelet coefficients in order and could generate multiple SR images at multiple scales sequentially. Experiments demonstrate that our WPSR could outperform existing state-of-the-art methods at multiple scales, especially for ×8 and ×16 scale factors.