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结合边缘检测的快速SIFT图像拼接方法

Fast SIFT image stitching algorithm combining edge detection

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摘要

为了对身管内壁序列图像进行精确配准与融合拼接, 得到大视场高分辨率待检测图像, 根据图像特点提出了一种结合边缘检测的快速SIFT图像拼接方法。该方法充分考虑待处理图像的特点, 首先对感兴趣区域的图像进行边缘检测, 分割出细节信息最丰富的子区域, 再对分割出的子区域提取SIFT特征点并进行配准。然后, 使用基于Sigmoid型函数权重的图像融合方法, 实现图像之间的无缝融合, 最大程度地保证了融合图像的清晰度和细节信息的完整性。实验结果表明: 改进的方法和传统SIFT算法相比, 在特征点提取阶段的平均效率提高了80%左右, 且整体配准阶段的效率也有较大提高。图像融合结果在主观评价和各种客观评价值上都能满足工程实际需求。

Abstract

In order to accurately register and stitch the sequence images of the inner wall of the barrel, and get a image with high field of view and high resolution, a fast SIFT image stitching method with edge detection according to the characteristics of the overlap region of the images was proposed. It took full account of the characteristics of the images and could quickly segment the sub-region that possessed the most abundant anomalous information by detecting the edge of the region of interest. Then, it extracted SIFT feature points of the sub-region and matched them accurately by RANSAC. After that, a novel fusion method based on the weight of Sigmoid function weight was used to realize the seamless fusion between sequence images. This method can maximize the clarity of the fused image and the integrity of the detailed information. Experimental results show that the improved algorithm is much less time-consuming than that of traditional SIFT algorithm. Its computational efficiency has improved about 80% in feature points extraction process and the efficiency of the whole registration process has been also improved. The subjective evaluation and the various objective evaluation values of the fusion results by this fusion method are superior to other fusion methods.

Newport宣传-MKS新实验室计划
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中图分类号:TP391

DOI:10.3788/irla201847.1126003

所属栏目:信息获取与辨识

基金项目:国家自然科学基金(61475113)

收稿日期:2017-10-30

修改稿日期:2017-12-08

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蔡怀宇:天津大学 精密仪器与光电子工程学院 光电信息技术教育部重点实验室, 天津 300072
武晓宇:天津大学 精密仪器与光电子工程学院 光电信息技术教育部重点实验室, 天津 300072
卓励然:天津大学 精密仪器与光电子工程学院 光电信息技术教育部重点实验室, 天津 300072
黄战华:天津大学 精密仪器与光电子工程学院 光电信息技术教育部重点实验室, 天津 300072
王星宇:天津大学 精密仪器与光电子工程学院 光电信息技术教育部重点实验室, 天津 300072

联系人作者:蔡怀宇(hycai@tju.edu.cn)

备注:蔡怀宇(1965-), 女, 教授, 博士, 主要从事光学技术与仪器、信息光学和图像处理等方面的研究。

【1】Zhang Peilin, Li Guozhang, Fu Jianping. Self-propelled Gun′s Fire System[M]. Beijing: Ordnance Industry Press, 2002. (in Chinese)
张培林, 李国章, 傅建平. 自行火炮火力系统[M]. 北京: 兵器工业出版社, 2002.

【2】Brown M, Lowe D G. Automatic panoramic image stitching using invariant features [J]. International Journal of Computer Vision, 2007, 74(1): 59-73.

【3】Li Yufeng, Li Guangze, Gu Shaohu, et al. Image mosaic algorithm based on area blocking and SIFT[J]. Optics and Precision Engineering, 2016, 24(5): 1197-1205. (in Chinese)
李玉峰, 李广泽, 谷绍湖, 等. 基于区域分块与尺度不变特征变换的图像拼接算法[J]. 光学 精密工程, 2016, 24(5): 1197-1205.

【4】Wang Xinhua, Huang Wei, Ouyang Jihong. Real-time image registration of the multi-detectors mosaic imaging system[J]. Chinese Optics, 2015, 8(2): 211-219. (in Chinese)
王新华, 黄玮, 欧阳继红. 多探测器拼接成像系统实时图像配准[J]. 中国光学, 2015, 8(2): 211-219.

【5】He Bin, Tao Dan, Peng Bo. High real-time F-SIFT image mosaic algorithm[J]. Infrared and Laser Engineering, 2013, 42(S2): 440-444. (in Chinese)
何宾, 陶丹, 彭勃. 高实时性F-SIFT图像拼接算法[J]. 红外与激光工程, 2013, 42(S2): 440-444.

【6】Zhang Y H, Jin X, Wang Z J. A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method [J]. Memetic Comp, 2017, 9: 231-244.

【7】Chen Yue, Zhao Yan, Wang Shigang. Fast image stitching method based on SIFT with adaptive local image feature [J]. Chinese Optics, 2016, 9(4): 415-422. (in Chinese)
陈月, 赵岩, 王世刚. 图像局部特征自适应的快速SIFT图像拼接方法[J]. 中国光学, 2016, 9(4): 415-422.

【8】Laraqui A, Baataoui A, Saaidi A, et al. Image mosaicing using voronoi diagram [J]. Multimed Tools Appl, 2017, 76: 8803-8829.

【9】Wang Dan, Liu Hui, Li Ke, et al. An image fusion algorithm based on trigonometric functions[J]. Infrared Technology, 2017, 39(1): 53-57. (in Chinese)
汪丹, 刘辉, 李可, 等.一种三角函数权重的图像拼接算法[J]. 红外技术, 2017, 39(1): 53-57.

【10】Chon J, Kim H, Lin C S. Seam-line determination for image mosaicking: A technique minimizing the maximum local mismatch and the global cost[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2010, 65(1): 86-92.

【11】Zhao Yan, Chen Yue, Wang Shigang, et al. Corrected fast SIFT image stitching method by combining projection error [J]. Optics and Precision Engineering, 2017, 25(6): 1645-1651. (in Chinese)
赵岩, 陈月, 王世刚, 等. 结合投影误差校正的快速SIFT图像拼接[J]. 光学 精密工程, 2017, 25(6): 1645-1651.

【12】Gonzalez Rafael C, Woods Richard E. Digital Image Processing[M]. 3rd ed. Translated by Ruan Qiuqi, Ruan Yuzhi, et al. Beijing: Electronic Industry Press, 2011. (in Chinese)
Gonzalez Rafael C, Woods Richard E. 数字图像处理[M].第三版. 阮秋琦, 阮宇智, 等译. 北京: 电子工业出版社, 2011.

【13】He Linyang, Liu Jinghong, Li Gang, et al. Fast image registration approach based on improved BRISK [J]. Infrared and Laser Engineering, 2014, 43(8): 2722-2727. (in Chinese)
何林阳, 刘晶红, 李刚, 等. 改进BRISK特征的快速图像配准算法[J]. 红外与激光程, 2014, 43(8): 2722-2727.

【14】Singh S, Singh R. Comparison of various edge detection techniques[C]//Proceedings of 2nd International Conference on Computing for Sustainable Global Development, IEEE, 2015: 393-396.

【15】He Li, Luo Yanfang. Research on face detection algorithm based oil digital image processing technology[J]. Computer Measurement & Control, 2017, 25(7): 273-281. (in Chinese)
何莉, 罗艳芳. 基于数字图像处理技术的人脸检测算法研究[J]. 计算机测量与控制, 2017, 25(7): 273-281.

【16】Kong Weiwei, Wang Binghe, Li Binbing, et al. Image Fusion: Multiresolution Non-subsampled[M]. Xi′an: Xidian University Press, 2015. (in Chinese)
孔韦韦, 王炳和, 李斌兵,等. 图像融合技术: 基于多分辨率非下采样理论与方法[M]. 西安: 西安电子科技大学出版社, 2015.

引用该论文

Cai Huaiyu,Wu Xiaoyu,Zhuo Liran,Huang Zhanhua,Wang Xingyu. Fast SIFT image stitching algorithm combining edge detection[J]. Infrared and Laser Engineering, 2018, 47(11): 1126003

蔡怀宇,武晓宇,卓励然,黄战华,王星宇. 结合边缘检测的快速SIFT图像拼接方法[J]. 红外与激光工程, 2018, 47(11): 1126003

被引情况

【1】刘 凯,汪 侃,杨晓梅,郑秀娟. 基于DoG检测图像特征点的快速二进制描述子. 光学 精密工程, 2020, 28(2): 485-496

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