摘要
为了解决当前图像局部模糊程度不一,导致图像复原效果欠佳,本文分别从图像特征点检测与滤波复原的角度出发.提出了基于SIFT特征点检测与维纳滤波的图像复原算法。根据尺度空间极值特性,进行关键点定位和方向分配,设计特征点描述子,得到模糊图像特征点分布,以建立圆盘复原模型中心坐标。基于点扩散圆盘函数特性,耦合傅里叶变换和最小二乘滤波,设计了无约束维纳滤波算子,达到图像复原处理的目的。根据特征角点定位,引导复原滤波圆盘函数计算起始位置,完成图像复原。实验测试结果显示,与当前复原算法相比,本算法拥有更高的复原视觉质量。
In order to solve the current local blur degree,resulting in poor effect of image restoration,this paper recovered from image feature point detection and filtering angle,proposed image restoration algorithm SIFT feature point detection and based on Wiener filter.First of all,according to the characteristics of the scale space extrema,key point positioning and direction of distribution,the design of feature descriptor,fuzzy image feature point distribution,to establish the coordinates of the center of the purpose of disc restoration model.Then,based on the characteristics of the point spread disk function,coupled with Fourier transform and least squares filtering,an unconstrained Wiener filter operator is designed to achieve the purpose of image restoration.Finally,according to the characteristics of the corner location to guide the restoration of the filter disk function to calculate the starting position,based on software engineering to achieve the restoration algorithm.The experimental results show that compared with the current restoration algorithm,the restoration technique has higher accuracy and stability.
作者
方小艳
Fang Xiaoyan(Shanxi Polytechnic College, Xianyang, Shanxi, 712000, China)
出处
《电子测量技术》
2017年第6期105-108,共4页
Electronic Measurement Technology
基金
陕西工业职业技术学院自然科学研究计划项目(ZK13-23)资助
关键词
图像复原
特征点检测
维纳滤波
圆盘函数
最小二乘滤波
傅里叶变换
image restoration
feature point detection
wiener filter
disk function
least square filter
fourier transform
