散斑噪声对基于特征点匹配的图像配准的影响  

Influence of Speckle Noise on Image Registration Based on Feature Point Matching

作　　者：陈波 孙天齐 刘爱新 

机构地区：[1]华北理工大学电气工程学院,河北唐山063210 [2]北京铁路局机务处,北京100086

出　　处：《激光与光电子学进展》2017年第12期179-184,共6页Laser & Optoelectronics Progress

基　　金：河北省自然科学基金(F2013209149);华北理工大学研究生创新项目(2017S24)

摘　　要：针对综合孔径数字全息,研究了散斑噪声对基于特征点匹配的图像配准精度的影响。建立了数字离轴全息实验装置,利用尺度不变特征变换(SIFT)算法和快速稳健特征(SURF)算法提取待配准图像和参考图像的特征点,采用欧氏距离最邻近法进行特征点匹配;在此基础上采用抽样一致性最大似然估计算法估计图像变换模型参数。实验结果表明,SIFT算法的抗噪性更好;对于SIFT算法,当散斑信噪比大于1.4时,配准相对误差小于0.02;对于SURF算法,当散斑信噪比大于1.7时,配准相对误差小于0.05。For the synthetic aperture digital holography, the effects of speckle noise on image registration accuracy based on the feature point matching are investigated. An experimental setup for the digital off-axis holography is established, and the scale-invariant feature transform （SIFT） algorithm and the speeded-up robust feature （SURF） algorithm are used to extract the feature points of the image to be matched and the reference image. The feature point matching is performed by the nearest neighbor search method based on Euclidean distance and the parameters of the image transformation model are estimated using the maximum likelihood estimation sample consensus algorithm. The experimental results show that the SIFT algorithm is more robust to speckle noise. For the SIFT algorithm, when the signal-to-noise ratio is greater than 1.4, the relative error of registration is less than 0.02, and for the SURF algorithm, when the signal-to-noise ratio is greater than 1.7, the relative error of registration is less than 0.05.

关 键 词：图像处理 图像配准 散斑噪声 特征点匹配 尺度不变特征变换算法 快速稳健特征算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]