{"title":"高强度图像相位恢复的自适应算法","authors":"Gilad Avidor, E. Gur","doi":"10.1109/IPTA.2010.5586791","DOIUrl":null,"url":null,"abstract":"In this paper, we present an adaptive Gerchberg-Saxton algorithm for phase retrieval. One of the drawbacks of the original Gerchberg-Saxton algorithm is the poor results it yields for very bright images. In this paper we demonstrate how a dynamic phase retrieval approach can improve the correlation between the required image and the reconstructed image by up to 10 percent. The paper gives explicit explanations to the principle behind the algorithm and shows experimental results to support the dynamic approach.","PeriodicalId":236574,"journal":{"name":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An adaptive algorithm for phase retrieval from high intensity images\",\"authors\":\"Gilad Avidor, E. Gur\",\"doi\":\"10.1109/IPTA.2010.5586791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present an adaptive Gerchberg-Saxton algorithm for phase retrieval. One of the drawbacks of the original Gerchberg-Saxton algorithm is the poor results it yields for very bright images. In this paper we demonstrate how a dynamic phase retrieval approach can improve the correlation between the required image and the reconstructed image by up to 10 percent. The paper gives explicit explanations to the principle behind the algorithm and shows experimental results to support the dynamic approach.\",\"PeriodicalId\":236574,\"journal\":{\"name\":\"2010 2nd International Conference on Image Processing Theory, Tools and Applications\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Conference on Image Processing Theory, Tools and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPTA.2010.5586791\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Image Processing Theory, Tools and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPTA.2010.5586791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive algorithm for phase retrieval from high intensity images
In this paper, we present an adaptive Gerchberg-Saxton algorithm for phase retrieval. One of the drawbacks of the original Gerchberg-Saxton algorithm is the poor results it yields for very bright images. In this paper we demonstrate how a dynamic phase retrieval approach can improve the correlation between the required image and the reconstructed image by up to 10 percent. The paper gives explicit explanations to the principle behind the algorithm and shows experimental results to support the dynamic approach.