{"title":"QR Iterative Subspace Identification and Its Application in Image Denoising","authors":"Chanzi Liu, Qingchun Chen","doi":"10.1109/ICIG.2011.176","DOIUrl":null,"url":null,"abstract":"The foundation of compressed sensing (CS) is the sparse representation of signals. Over-complete dictionaries could be utilized to map signals into their sparse representation over the dictionary. And iterative subspace identification (ISI) is an effective algorithm to determine the over-complete dictionary from signal samples. In this paper, the QR decomposition is proposed to be employed in the ISI scheme so as to obtain the adaptive over-complete dictionary. It is shown that the QR-ISI outperforms the ISI in terms of the recovered PSNR. Finally, the QR-ISI method could be applied to image denoising. Experiment results are presented to show that the QR-ISI offers a feasible method for image denoising with reasonable performance.","PeriodicalId":277974,"journal":{"name":"2011 Sixth International Conference on Image and Graphics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Sixth International Conference on Image and Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIG.2011.176","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
The foundation of compressed sensing (CS) is the sparse representation of signals. Over-complete dictionaries could be utilized to map signals into their sparse representation over the dictionary. And iterative subspace identification (ISI) is an effective algorithm to determine the over-complete dictionary from signal samples. In this paper, the QR decomposition is proposed to be employed in the ISI scheme so as to obtain the adaptive over-complete dictionary. It is shown that the QR-ISI outperforms the ISI in terms of the recovered PSNR. Finally, the QR-ISI method could be applied to image denoising. Experiment results are presented to show that the QR-ISI offers a feasible method for image denoising with reasonable performance.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
