{"title":"调制传递函数与神经网络噪声测量","authors":"J. Delvit, D. Léger, S. Roques, C. Valorge","doi":"10.1109/NNSP.2003.1318011","DOIUrl":null,"url":null,"abstract":"In the context of Earth observation satellites such as SPOT or IKONOS, it is important to measure the modulation transfer function (MTF) and the noise in order to quantify the quality of the imaging system. This measurement is useful to decide to focus the telescope or to make a deconvolution filter whose purpose is to enhance image contrast. This paper presents a univariant MTF and noise measurement method using non specific views. It is a particular application of a general approach of image quality assessment. The method presented in this paper is based on artificial neural network (ANN) use. The ANN learns how to recognize MTF and noise from known images, and the neural network is able, after the learning step, to assess the MTF and the noise from unknown images.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"37 19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Modulation transfer function and noise measurement using neural networks\",\"authors\":\"J. Delvit, D. Léger, S. Roques, C. Valorge\",\"doi\":\"10.1109/NNSP.2003.1318011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of Earth observation satellites such as SPOT or IKONOS, it is important to measure the modulation transfer function (MTF) and the noise in order to quantify the quality of the imaging system. This measurement is useful to decide to focus the telescope or to make a deconvolution filter whose purpose is to enhance image contrast. This paper presents a univariant MTF and noise measurement method using non specific views. It is a particular application of a general approach of image quality assessment. The method presented in this paper is based on artificial neural network (ANN) use. The ANN learns how to recognize MTF and noise from known images, and the neural network is able, after the learning step, to assess the MTF and the noise from unknown images.\",\"PeriodicalId\":315958,\"journal\":{\"name\":\"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)\",\"volume\":\"37 19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2003.1318011\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2003.1318011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modulation transfer function and noise measurement using neural networks
In the context of Earth observation satellites such as SPOT or IKONOS, it is important to measure the modulation transfer function (MTF) and the noise in order to quantify the quality of the imaging system. This measurement is useful to decide to focus the telescope or to make a deconvolution filter whose purpose is to enhance image contrast. This paper presents a univariant MTF and noise measurement method using non specific views. It is a particular application of a general approach of image quality assessment. The method presented in this paper is based on artificial neural network (ANN) use. The ANN learns how to recognize MTF and noise from known images, and the neural network is able, after the learning step, to assess the MTF and the noise from unknown images.