{"title":"基于功率谱分析的图像定义函数研究","authors":"Guojin Chen, Miaofen Zhu, Kesong Zhang","doi":"10.1109/ROSE.2007.4373977","DOIUrl":null,"url":null,"abstract":"The image that is in focus has the sharper edge. The edge information gotten by Fourier transform corresponds to the higher part in the frequency domain. So the value of the image power-spectra can be used to represent the image definition. This paper analyses the essential property of the image power-spectra. The study indicates that the curves of the image power-spectra are similar in spite of different image contents. The image definition function has been constructed based on the definition of the image power-spectra. And the simulation indicates that the image definition function is obvious, no-bias, single-peaked in variation, high in the ratio of signal to noise, little in the computation quantity, excellent in the focusing performance and can reflect off-focus characteristics.","PeriodicalId":179874,"journal":{"name":"International Symposium on Robotic and Sensors Environments","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Image Definition Function Based on Power-spectra Analysis\",\"authors\":\"Guojin Chen, Miaofen Zhu, Kesong Zhang\",\"doi\":\"10.1109/ROSE.2007.4373977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The image that is in focus has the sharper edge. The edge information gotten by Fourier transform corresponds to the higher part in the frequency domain. So the value of the image power-spectra can be used to represent the image definition. This paper analyses the essential property of the image power-spectra. The study indicates that the curves of the image power-spectra are similar in spite of different image contents. The image definition function has been constructed based on the definition of the image power-spectra. And the simulation indicates that the image definition function is obvious, no-bias, single-peaked in variation, high in the ratio of signal to noise, little in the computation quantity, excellent in the focusing performance and can reflect off-focus characteristics.\",\"PeriodicalId\":179874,\"journal\":{\"name\":\"International Symposium on Robotic and Sensors Environments\",\"volume\":\"141 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\":\"International Symposium on Robotic and Sensors Environments\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROSE.2007.4373977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Robotic and Sensors Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROSE.2007.4373977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Study on Image Definition Function Based on Power-spectra Analysis
The image that is in focus has the sharper edge. The edge information gotten by Fourier transform corresponds to the higher part in the frequency domain. So the value of the image power-spectra can be used to represent the image definition. This paper analyses the essential property of the image power-spectra. The study indicates that the curves of the image power-spectra are similar in spite of different image contents. The image definition function has been constructed based on the definition of the image power-spectra. And the simulation indicates that the image definition function is obvious, no-bias, single-peaked in variation, high in the ratio of signal to noise, little in the computation quantity, excellent in the focusing performance and can reflect off-focus characteristics.
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