{"title":"降级的文件透渗删除","authors":"Róisín Rowley-Brooke, A. Kokaram","doi":"10.1109/IMVIP.2011.21","DOIUrl":null,"url":null,"abstract":"This paper presents a Bayesian approach for bleed-through reduction in degraded document images based on a simple linear degradation model. A variation of ICM optimisation is used whereby samples are drawn for the bleed-through reduced images, whilst the remaining variables are estimated via the mode of their conditional probabilities. The proposed method is tested on various samples of scanned manuscript images with different degrees of degradation, and the results show some convincing removal of bleed-through.","PeriodicalId":179414,"journal":{"name":"2011 Irish Machine Vision and Image Processing Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Degraded Document Bleed-Through Removal\",\"authors\":\"Róisín Rowley-Brooke, A. Kokaram\",\"doi\":\"10.1109/IMVIP.2011.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a Bayesian approach for bleed-through reduction in degraded document images based on a simple linear degradation model. A variation of ICM optimisation is used whereby samples are drawn for the bleed-through reduced images, whilst the remaining variables are estimated via the mode of their conditional probabilities. The proposed method is tested on various samples of scanned manuscript images with different degrees of degradation, and the results show some convincing removal of bleed-through.\",\"PeriodicalId\":179414,\"journal\":{\"name\":\"2011 Irish Machine Vision and Image Processing Conference\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Irish Machine Vision and Image Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMVIP.2011.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Irish Machine Vision and Image Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMVIP.2011.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a Bayesian approach for bleed-through reduction in degraded document images based on a simple linear degradation model. A variation of ICM optimisation is used whereby samples are drawn for the bleed-through reduced images, whilst the remaining variables are estimated via the mode of their conditional probabilities. The proposed method is tested on various samples of scanned manuscript images with different degrees of degradation, and the results show some convincing removal of bleed-through.
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