{"title":"二维反卷积的迭代算法","authors":"Zhang Shengfu, Shen Haiming, Zhao Huichang, Zhao Zhilin","doi":"10.1109/ICSIGP.1996.567044","DOIUrl":null,"url":null,"abstract":"This paper discusses a finite iterative method for computing 2-D deconvolution. The given algorithm doesn't have any convergent problems and doesn't deal with any complex irrational factors or know all of x(n/sub 1/, n/sub 2/) or y(n/sub 1/, n/sub 2/). So it can be called a semi-blind algorithm for 2-D deconvolution. A flow diagram of the algorithm, comparison of computational cost and FFT are given.","PeriodicalId":385432,"journal":{"name":"Proceedings of Third International Conference on Signal Processing (ICSP'96)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An iterative algorithm for 2-D deconvolution\",\"authors\":\"Zhang Shengfu, Shen Haiming, Zhao Huichang, Zhao Zhilin\",\"doi\":\"10.1109/ICSIGP.1996.567044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses a finite iterative method for computing 2-D deconvolution. The given algorithm doesn't have any convergent problems and doesn't deal with any complex irrational factors or know all of x(n/sub 1/, n/sub 2/) or y(n/sub 1/, n/sub 2/). So it can be called a semi-blind algorithm for 2-D deconvolution. A flow diagram of the algorithm, comparison of computational cost and FFT are given.\",\"PeriodicalId\":385432,\"journal\":{\"name\":\"Proceedings of Third International Conference on Signal Processing (ICSP'96)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Third International Conference on Signal Processing (ICSP'96)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSIGP.1996.567044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Third International Conference on Signal Processing (ICSP'96)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIGP.1996.567044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper discusses a finite iterative method for computing 2-D deconvolution. The given algorithm doesn't have any convergent problems and doesn't deal with any complex irrational factors or know all of x(n/sub 1/, n/sub 2/) or y(n/sub 1/, n/sub 2/). So it can be called a semi-blind algorithm for 2-D deconvolution. A flow diagram of the algorithm, comparison of computational cost and FFT are given.
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