{"title":"A new image restoration approach for imaging through the atmosphere","authors":"K. K. Halder, M. Tahtali, S. Anavatti","doi":"10.1109/ISSPIT.2013.6781906","DOIUrl":null,"url":null,"abstract":"Restoration of a sequence of images influenced by atmospheric turbulence is a challenging task. A new approach for geometrical corrections and noise cancelations of the turbulence degraded frames of a video is presented. The time-averaged frame of the video is used to overcome the geometric deformations through an iterative robust image registration technique. The results of the registration are geometrically corrected frames which still contain noise. A non-local means filter is used to extract the noise from the individual frames. A performance comparison between our proposed restoration approach and the earlier First Register Then Average And Subtract (FRTAAS) approach is presented in terms of restoration accuracy. The effectiveness of our approach is demonstrated on both synthetic and real-world surveillance videos.","PeriodicalId":88960,"journal":{"name":"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology","volume":"55 1","pages":"000350-000355"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2013.6781906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Restoration of a sequence of images influenced by atmospheric turbulence is a challenging task. A new approach for geometrical corrections and noise cancelations of the turbulence degraded frames of a video is presented. The time-averaged frame of the video is used to overcome the geometric deformations through an iterative robust image registration technique. The results of the registration are geometrically corrected frames which still contain noise. A non-local means filter is used to extract the noise from the individual frames. A performance comparison between our proposed restoration approach and the earlier First Register Then Average And Subtract (FRTAAS) approach is presented in terms of restoration accuracy. The effectiveness of our approach is demonstrated on both synthetic and real-world surveillance videos.