{"title":"Compressive sensing multi-spectral demosaicing from single sensor architecture","authors":"H. Aggarwal, A. Majumdar","doi":"10.1109/ChinaSIP.2014.6889259","DOIUrl":null,"url":null,"abstract":"This paper addresses the recovery of multi-spectral images from single sensor cameras using compressed sensing (CS) techniques. It is an exploratory work since this particular problem has not been addressed before. We considered two types of sensor arrays - uniform and random; and two recovery approaches - Kronecker CS (KCS) and group-sparse reconstruction. Two sets of experiments were carried out. From the first set of experiments we find that both KCS and group-sparse recovery yields good results for random sampling, but for uniform sampling only KCS yields good results. In the second set of experiments we compared our proposed techniques with state-of-the-art methods. We find that our proposed methods yields considerable better results.","PeriodicalId":248977,"journal":{"name":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ChinaSIP.2014.6889259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27
Abstract
This paper addresses the recovery of multi-spectral images from single sensor cameras using compressed sensing (CS) techniques. It is an exploratory work since this particular problem has not been addressed before. We considered two types of sensor arrays - uniform and random; and two recovery approaches - Kronecker CS (KCS) and group-sparse reconstruction. Two sets of experiments were carried out. From the first set of experiments we find that both KCS and group-sparse recovery yields good results for random sampling, but for uniform sampling only KCS yields good results. In the second set of experiments we compared our proposed techniques with state-of-the-art methods. We find that our proposed methods yields considerable better results.