电子设计工程
電子設計工程
전자설계공정
ELECTRONIC DESIGN ENGINEERING
2015年
9期
127-130,134
,共5页
稀疏表示%超分辨率%字典学习%星图识别%质心细分
稀疏錶示%超分辨率%字典學習%星圖識彆%質心細分
희소표시%초분변솔%자전학습%성도식별%질심세분
sparse representation%super resolution%dictionary learning%star Identification%subdivided Locating
本文提出了一种基于超分辨率图像重建的质心细分定位的新方法。在图像识别与匹配中,往往需要用到物理、数字等的特征提取方法,当给定的图像分辨率低时,就会使得所提取出的特征产生不可忽略的误差。为了解决这一问题,本文以实拍星图分辨率低的局限性为例,并结合传统的质心提取方法得到观测星图中任意两颗星的角距,验证新方法降低误差的有效性。实验结果表明,在同等系统误差条件下,相对于原始星图求得的星角距,基于超分辨率重建后的星图所得到的观测星的角距值更接近于真实角距,精度提高了29.56%,即新方法提取到的特征更加精确。
本文提齣瞭一種基于超分辨率圖像重建的質心細分定位的新方法。在圖像識彆與匹配中,往往需要用到物理、數字等的特徵提取方法,噹給定的圖像分辨率低時,就會使得所提取齣的特徵產生不可忽略的誤差。為瞭解決這一問題,本文以實拍星圖分辨率低的跼限性為例,併結閤傳統的質心提取方法得到觀測星圖中任意兩顆星的角距,驗證新方法降低誤差的有效性。實驗結果錶明,在同等繫統誤差條件下,相對于原始星圖求得的星角距,基于超分辨率重建後的星圖所得到的觀測星的角距值更接近于真實角距,精度提高瞭29.56%,即新方法提取到的特徵更加精確。
본문제출료일충기우초분변솔도상중건적질심세분정위적신방법。재도상식별여필배중,왕왕수요용도물리、수자등적특정제취방법,당급정적도상분변솔저시,취회사득소제취출적특정산생불가홀략적오차。위료해결저일문제,본문이실박성도분변솔저적국한성위례,병결합전통적질심제취방법득도관측성도중임의량과성적각거,험증신방법강저오차적유효성。실험결과표명,재동등계통오차조건하,상대우원시성도구득적성각거,기우초분변솔중건후적성도소득도적관측성적각거치경접근우진실각거,정도제고료29.56%,즉신방법제취도적특정경가정학。
A novel method is put forward in subdividing locating of star image in this paper, which is based on super resolution image reconstruction that can generate high resolution image with more information contained by using one or more low resolution images. When the resolution of given image is low, it makes the features extracted errors not be ignored. In order to solve this problem, taking the limitations of real star map with low resolution for instance, we use the traditional method of centroid extraction to obtain any two stars’ angular distance in star map, verifying the effectiveness of the new method in reducing errors. By comparing the angular distances with real angular distances in star database, the results show that angular distance achieved from super resolution reconstruction star map is closer to real value, with the precision 29.56% improved than that from original star map.