{"title":"图像恢复的行和块动作投影技术","authors":"C. Podilchuk, R. Mammone","doi":"10.1364/srs.1989.wb2","DOIUrl":null,"url":null,"abstract":"The discrete image restoration problem consists of solving a set of linear equations where g represents the measured image, the M × N matrix H denotes the degradation operator and f represents the image to be determined. Since H is usually an ill-conditioned or rank-deficient matrix, solving the set of equations is not straightforward. In addition, the set of linear equations are inconsistent due to measurement errors, quantization noise, and inaccuracies in modeling. Therefore, direct inversion techniques are not applicable.","PeriodicalId":193110,"journal":{"name":"Signal Recovery and Synthesis III","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Row and Block Action Projection Techniques for Image Restoration\",\"authors\":\"C. Podilchuk, R. Mammone\",\"doi\":\"10.1364/srs.1989.wb2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The discrete image restoration problem consists of solving a set of linear equations where g represents the measured image, the M × N matrix H denotes the degradation operator and f represents the image to be determined. Since H is usually an ill-conditioned or rank-deficient matrix, solving the set of equations is not straightforward. In addition, the set of linear equations are inconsistent due to measurement errors, quantization noise, and inaccuracies in modeling. Therefore, direct inversion techniques are not applicable.\",\"PeriodicalId\":193110,\"journal\":{\"name\":\"Signal Recovery and Synthesis III\",\"volume\":\"22 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\":\"Signal Recovery and Synthesis III\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/srs.1989.wb2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Recovery and Synthesis III","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/srs.1989.wb2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Row and Block Action Projection Techniques for Image Restoration
The discrete image restoration problem consists of solving a set of linear equations where g represents the measured image, the M × N matrix H denotes the degradation operator and f represents the image to be determined. Since H is usually an ill-conditioned or rank-deficient matrix, solving the set of equations is not straightforward. In addition, the set of linear equations are inconsistent due to measurement errors, quantization noise, and inaccuracies in modeling. Therefore, direct inversion techniques are not applicable.