The proposed algorithms are applied in synthetic and real image sequences , and the experimental results are prospective and satisfied 所提出的算法用于实际的图像序列，取得了满意的超分辨率效果。
Two of the key steps of this technology are the establishment of observation model and image registration ( motion estimation ) 观测模型的建立和图像配准(运动估计)是超分辨率图像融合的关键环节。
Therefore , the spatial resolution afforded by the optics ca n ' t be fully utilized in such imaging systems 超分辨率图像复原技术可以利用视频图像序列中各帧之间的冗余信息，重构出超分辨率图像，消除和降低混频效应。
Super - resolution refers to obtaining video at a resolution higher than that of the camera ( sensor ) used in recording the image 超分辨率( superresolution )涉及到获取高于记录图像中使用的摄像机(传感器)分辨率来获取视频问题。
The technology of super resolution in image sequences uses superfluous information among image sequences to rebuild an image with higher resolution 图像序列超分辨率处理就是利用图像序列中帧与帧之间存在的互补信息,重构一幅高分辨率图像。
The accurate subpixel motion information is indispensable to the process of superresolution image reconstruction from a sequence of low resolution images 利用低分辨率图象序列宋重建超分辨率图象时，精确可信的亚象素级运动信息是必不可少的。
We find the method of inversion analysis is a more effective and convenient one in these algorisms . the third step , we propose two methods for image capturing 然后分析了各种超分辨率复原算法，最后得出了较简单易行的，能从真正意义上提高分辨率的算法?反演解析法，引出课题。
Thus , this thesis secondly sets up the observation model , and then provides a detailed introduction to the principle , classification , and manipulation of image registration 因此,本文接着建立了超分辨率图像成像的观测模型,对图像配准的原理、分类以及具体方法进行详细的介绍。
The thesis introduces the background , development status and object of our research firstly . the second step , many kinds of super - resolution reconstruction algorithms are discussed 论文首先介绍了课题的背景，超分辨率图像重构在国内外的发展状况及本课题要解决的问题和达到的要求。
Interframe superresolution methods exploit this additional information , contained in multiple frames , to reconstruct a high - resolution still image or a sequence o high - resolution images 帧间超分辨率方法利用了这个包含在多帧中的附加信息去重构一个高分辨率静态图像或一个高分辨率图像序列。
