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dc.contributor.authorXiao, Quanzh_CN
dc.contributor.authorDing, Xinghaozh_CN
dc.contributor.authorQian, Kunzh_CN
dc.contributor.authorWang, Xinxinzh_CN
dc.contributor.author丁兴号zh_CN
dc.date.accessioned2015-07-22T02:39:14Z
dc.date.available2015-07-22T02:39:14Z
dc.date.issued2009zh_CN
dc.identifier.citationINTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL SCIENCES AND OPTIMIZATION, VOL 1, PROCEEDINGS, 2009:892-895zh_CN
dc.identifier.otherWOS:000273548200206zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86438
dc.descriptionConference Name:2nd International Joint Conference on Computational Sciences and Optimization. Conference Address: Sanya, PEOPLES R CHINA. Time:APR 24-26, 2009.zh_CN
dc.description.abstractIn this paper, a new method for multi-frame super-resolution reconstruction is proposed It builds up with three steps: motion estimation, frame interpolation and fusion, deblurring. Based on the pure translations motion model, a simple motion estimation method is firstly proposed All measured frames are subsequently located to a fixed grid and interpolated using an effective technique called Steering Kernel Regression. Then, the reconstruction frame is obtained by fusion high-resolution pixels selected from all of the interpolated frames. Finally, the reconstructed frame is deblurred at the last step. Experimental results on simulate and real data confirm the effectiveness of the proposed method and demonstrate the Steering Kernel Regression's superiority to other interpolate methods such as cubic interpolation and the method of [9].zh_CN
dc.language.isoen_USzh_CN
dc.publisherIEEE COMPUTER SOCzh_CN
dc.source.urihttp://dx.doi.org/10.1109/CSO.2009.354zh_CN
dc.subjectIMAGE-RECONSTRUCTIONzh_CN
dc.subjectALGORITHMzh_CN
dc.titleA New Method for Multi-Frame Super-Resolution Reconstructionzh_CN
dc.typeConferencezh_CN


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