{"title":"基于自适应正则化的高频成分缺失图像迭代恢复","authors":"J. Maeda, K. Murata","doi":"10.1364/srs.1986.fb3","DOIUrl":null,"url":null,"abstract":"The problem of restoring the details of bandlimited images of spatial finite extent or recovering the missing high-frequency components has recently been discussed extensively. Since the present problem is ill-conditioned, certain types of regularization techniques are required [1-5]. Moreover, some types of a priori information or constraints are used to overcome the ambiguity and instability of the solution [6-9].","PeriodicalId":262149,"journal":{"name":"Topical Meeting On Signal Recovery and Synthesis II","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Iterative Restoration of Images with Missing High-Frequency Components Using Adaptive Regularization\",\"authors\":\"J. Maeda, K. Murata\",\"doi\":\"10.1364/srs.1986.fb3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of restoring the details of bandlimited images of spatial finite extent or recovering the missing high-frequency components has recently been discussed extensively. Since the present problem is ill-conditioned, certain types of regularization techniques are required [1-5]. Moreover, some types of a priori information or constraints are used to overcome the ambiguity and instability of the solution [6-9].\",\"PeriodicalId\":262149,\"journal\":{\"name\":\"Topical Meeting On Signal Recovery and Synthesis II\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topical Meeting On Signal Recovery and Synthesis II\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/srs.1986.fb3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topical Meeting On Signal Recovery and Synthesis II","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/srs.1986.fb3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Iterative Restoration of Images with Missing High-Frequency Components Using Adaptive Regularization
The problem of restoring the details of bandlimited images of spatial finite extent or recovering the missing high-frequency components has recently been discussed extensively. Since the present problem is ill-conditioned, certain types of regularization techniques are required [1-5]. Moreover, some types of a priori information or constraints are used to overcome the ambiguity and instability of the solution [6-9].
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