{"title":"Noise filtering of periodic image sequences","authors":"A. Plebe","doi":"10.1109/ICIAP.2001.957004","DOIUrl":null,"url":null,"abstract":"This work describes a method for filtering image sequences degraded by noise, where the main object is moving with an almost periodic displacement. This object is assumed to be the only region of interest in the image, and tracking its movement against the background is the goal of the image processing. Under such circumstances, it is argued that a noise reduction strategy based on the knowledge of the motion will be more efficient than other classical methods for dynamic image sequences. This kind of problem is not unusual in the processing of scientific images, especially in the medical field. In this case the presence of noise is critical not only for the degradation of the visual quality, but also for the effectiveness of subsequent processing tasks, such as analysis and clinical interpretation.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 11th International Conference on Image Analysis and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAP.2001.957004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This work describes a method for filtering image sequences degraded by noise, where the main object is moving with an almost periodic displacement. This object is assumed to be the only region of interest in the image, and tracking its movement against the background is the goal of the image processing. Under such circumstances, it is argued that a noise reduction strategy based on the knowledge of the motion will be more efficient than other classical methods for dynamic image sequences. This kind of problem is not unusual in the processing of scientific images, especially in the medical field. In this case the presence of noise is critical not only for the degradation of the visual quality, but also for the effectiveness of subsequent processing tasks, such as analysis and clinical interpretation.