改进SIFT快速图像拼接和重影优化

刘杰; 游品鸿; 占建斌; 刘金凤

doi:10.37188/OPE.20202809.2076

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改进SIFT快速图像拼接和重影优化

信息科学 | 更新时间：2020-09-28

- 改进SIFT快速图像拼接和重影优化
- Improved SIFT fast image stitching and ghosting optimization algorithm
- 光学精密工程 2020年28卷第9期页码：2076-2084
- 作者机构：
  
  1.哈尔滨理工大学电气与电子工程学院, 黑龙江哈尔滨 150080
  2.工程电介质及应用技术教育部重点实验室(哈尔滨理工大学), 黑龙江哈尔滨 150080
- 作者简介：
  
  [ "刘杰(1980-), 女, 黑龙江哈尔滨人, 副教授, 2005年于哈尔滨工业大学获得硕士学位, 2013年于哈尔滨理工大学获得博士学位, 主要从事光纤光栅传感及解调技术, 数字图像处理及计算机视觉技术方面的研究。E-mail:liujie@hrbust.edu.cn" ]
  [ "游品鸿(1995-), 男, 福建福州人, 硕士, 2018年于黑龙江八一农垦大学获得学士学位, 主要从事计算机视觉以及数字图像处理的研究。E-mail:1091453221@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(51607049);黑龙江省自然科学基金资助项目(LH2019E067)
- DOI：10.37188/OPE.20202809.2076
  中图分类号： TP391
- 收稿日期：2020-03-23，
  
  修回日期：2020-05-02，
  
  录用日期：2020-5-2，
  
  纸质出版日期：2020-09-25
- 稿件说明：
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刘杰, 游品鸿, 占建斌, 等. 改进SIFT快速图像拼接和重影优化[J]. 光学精密工程, 2020,28(9):2076-2084.

Jie LIU, Pin-hong YOU, Jian-bin ZHAN, et al. Improved SIFT fast image stitching and ghosting optimization algorithm[J]. Optics and precision engineering, 2020, 28(9): 2076-2084.
刘杰, 游品鸿, 占建斌, 等. 改进SIFT快速图像拼接和重影优化[J]. 光学精密工程, 2020,28(9):2076-2084. DOI： 10.37188/OPE.20202809.2076.

Jie LIU, Pin-hong YOU, Jian-bin ZHAN, et al. Improved SIFT fast image stitching and ghosting optimization algorithm[J]. Optics and precision engineering, 2020, 28(9): 2076-2084. DOI： 10.37188/OPE.20202809.2076.

摘要

针对图像匹配实时性受限的问题以及在图像投影拼接中出现的重影的问题，提出了一种改进尺度不变特征变换（SIFT）快速图像拼接和重影优化算法。该算法通过图像之间共享信息量的相似性进行特征点的区域划分，利用SIFT算法对相似重合区域进行特征点的检测以及提取，减少无用区域的算法运算时间；在图像拼接的阶段，通过特征点计算投影矩阵，并进行粗投影，再根据特征点所在区域密集程度，通过最佳拟合变换对特征点密集区域进行二次投影拼接，减少拼接图像出现重影的问题。实验结果表明，该算法与传统的SIFT算法相比，在特征点提取效率提高了大约58%。在图像拼接结果上，通过客观评价指标进行比较提高大约10%。

Abstract

This study aims to address the real-time limitations of image matching and the problem of ghosting in image projection stitching. Hence

a fast and improved scale invariant feature transform (SIFT) image stitching and ghosting optimization algorithm was proposed. First

feature points were classified based on the similarity of the shared information between the images

and then

the SIFT algorithm was used to detect and extract the feature points of similar coincident regions. This approach required the algorithm to spend less time on the useless regions. At the image stitching stage

the projection matrix was calculated by feature points

and rough projection was performed. Thereafter

according to the density of the area where the feature points were located

secondary projection splicing was performed on the dense feature points area by optimal fitting transformation to reduce the ghosting problem. Experiments are performed

and the results demonstrate that compared with the traditional SIFT algorithm

the efficiency of feature point extraction is improved by approximately 58%. Similarly

the comparison by an objective evaluation index show that image stitching improved by approximately 10%.

关键词

Keywords

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