{"title":"基于时空图像处理的图像序列伴随噪声去除","authors":"A. Yamashita, Isao Fukuchi, T. Kaneko, K. Miura","doi":"10.1109/ROBOT.2008.4543570","DOIUrl":null,"url":null,"abstract":"This paper describes a method for removing adherent noises from image sequences. In outdoor environments, it is often the case that scenes taken by a camera are deteriorated because of adherent noises such as waterdrops on the surface of the lens-protecting glass of the camera. To solve this problem, our method takes advantage of image sequences captured with a moving camera. The method makes a spatio-temporal image to extract the regions of adherent noises by examining differences of track slopes in cross section images between adherent noises and other objects. Finally, regions of noises are eliminated by replacing with image data corresponding to object regions. Experimental results show the effectiveness of our method.","PeriodicalId":351230,"journal":{"name":"2008 IEEE International Conference on Robotics and Automation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Removal of adherent noises from image sequences by spatio-temporal image processing\",\"authors\":\"A. Yamashita, Isao Fukuchi, T. Kaneko, K. Miura\",\"doi\":\"10.1109/ROBOT.2008.4543570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a method for removing adherent noises from image sequences. In outdoor environments, it is often the case that scenes taken by a camera are deteriorated because of adherent noises such as waterdrops on the surface of the lens-protecting glass of the camera. To solve this problem, our method takes advantage of image sequences captured with a moving camera. The method makes a spatio-temporal image to extract the regions of adherent noises by examining differences of track slopes in cross section images between adherent noises and other objects. Finally, regions of noises are eliminated by replacing with image data corresponding to object regions. Experimental results show the effectiveness of our method.\",\"PeriodicalId\":351230,\"journal\":{\"name\":\"2008 IEEE International Conference on Robotics and Automation\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Robotics and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBOT.2008.4543570\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Robotics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBOT.2008.4543570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Removal of adherent noises from image sequences by spatio-temporal image processing
This paper describes a method for removing adherent noises from image sequences. In outdoor environments, it is often the case that scenes taken by a camera are deteriorated because of adherent noises such as waterdrops on the surface of the lens-protecting glass of the camera. To solve this problem, our method takes advantage of image sequences captured with a moving camera. The method makes a spatio-temporal image to extract the regions of adherent noises by examining differences of track slopes in cross section images between adherent noises and other objects. Finally, regions of noises are eliminated by replacing with image data corresponding to object regions. Experimental results show the effectiveness of our method.