改进Noble算子匹配的电子稳像法

初守艳; 席志红

doi:10.3788/OPE.20142201.0204

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改进Noble算子匹配的电子稳像法

信息科学 | 更新时间：2020-08-13

- 改进Noble算子匹配的电子稳像法
- Digital image stabilization based on improved Noble feature matching
- 光学精密工程 2014年22卷第1期页码：204-212
- 作者机构：
  
  哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(60875025/f030410)();中央高校基本科研业务费专项资金资助课题()
- DOI：10.3788/OPE.20142201.0204
  中图分类号： TP391.4
- 收稿日期：2013-05-23，
  
  纸质出版日期：2014-01-15
- 稿件说明：
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初守艳, 席志红,. 改进Noble算子匹配的电子稳像法[J]. 光学精密工程, 2014,22(1): 204-212

CHU Shou-yan, XI Zhi-hong,. Digital image stabilization based on improved Noble feature matching[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 204-212
初守艳, 席志红,. 改进Noble算子匹配的电子稳像法[J]. 光学精密工程, 2014,22(1): 204-212 DOI： 10.3788/OPE.20142201.0204.

CHU Shou-yan, XI Zhi-hong,. Digital image stabilization based on improved Noble feature matching[J]. Editorial Office of Optics and Precision Engineering, 2014,22(1): 204-212 DOI： 10.3788/OPE.20142201.0204.

摘要

考虑全局运动估计算法对电子稳像系统准确性和实时性的影响

提出了改进Noble算子匹配的电子稳像算法。该算法通过区域预选、特征匹配、参数求解和运动补偿四步操作稳定抖动视频。首先

根据全局运动一致性的特点

采用子块间的绝对差值提前剔除部分不可靠区域

并改进传统Noble算子的全局阈值部分

根据区域梯度均值自适应调整阈值

检测各保留区域的角点

以保证图像特征的空间均匀性分布;其次

根据特征点的邻域信息构造梯度方向描述子进行粗匹配运算

并利用最近邻次近邻比率和均值距离准则两步操作优化匹配集合;然后

结合运动模型求解全局参数;最后

选择卡尔曼滤波过程完成抖动图像的补偿处理。Matlab仿真结果证明:该算法能平均提高峰值信噪比2 dB以上

当旋转角度小于5°时性能更为优异

能准确、快速地稳定视频图像。

Abstract

In consideration of the effect of global motion estimation algorithm on the real-time performance and the accuracy of a electron stabilization system

a digital image stabilization algorithm based on the improved Noble feature matching was proposed. The algorithm was composed of four steps:preselecting regions

feature matching

solving parameters and motion compensation. Firstly

the global consistency of the background pixels was used to eliminate the unreliable regions

and the adaptive threshold was added into the traditional Noble feature extraction process to ensure the features uniformly distributed in the retained image regions. Then

the neighborhood gradient information of the feature point was used to build the feature descriptors and to get the rough match sets. At the same time

the ratio of the closest neighbor to second-closest neighbor and the mean space distance criteria were both used to optimize the match sets. Furthermore

the global motion parameters were solved. Finally

the Kalman filter was used to get the true shaky components to compensate the images. Matlab experimental results indicate that the algorithm can increase the peak signal-to-noise ratio more than 2 dB

and it is especially excellent when the rotation angle is less than 5 °. In conclusion

the algorithm can stabilize video images quickly and accurately.

关键词

Keywords

references

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