Four-phase-VISAR images registration method
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摘要: 为了实现二维VISAR四相位图像配准，提出了一种基于SIFT算法的图像配准方法。首先利用SIFT算法分别提取基准图像和待配准图像的特征点，这一步中充分考虑了二维VISAR图像本身的特点，通过引入同源的无干涉图像获得了更准确的提取结果。接着对特征点进行粗匹配，并进一步设计了角度直方图和特征点距离两步筛选法进行精匹配。然后，根据最终的匹配结果计算变换矩阵，最后将变换矩阵应用于待配准的图像进行插值变换实现图像配准。以四相位图像的其中一幅作为基准对剩余三幅图像进行配准，实验结果表明：无条纹图像的相关性从0.5提升至0.9，有条纹图像的截断相位计算精度有了大幅提升，有效解决了二维VISAR四相位图像的配准问题。Abstract: In order to achieve four-phase images registration, an image registration method based on SIFT (Scale Invariant Features Transform) algorithm is proposed in this paper. The method is divided into four steps. Firstly, the feature points of the reference image and the misregistration images are extracted respectively. In this step, the characteristics of 2D-VISAR images are fully considered and homologous non-fringe images are introduced to obtain more accurate results. The second step is feature points matching. After roughly matching, the two-step-filtering method composed by angle histograms and feature point distance is designed to achieve accurate matching. The third step involves calculating the transformation parameters based on the final matching results. Finally, the transformation parameters are applied to misregistration images for interpolation transformation to achieve image registration. One of the four-phase images is used as the reference image to register the remaining three images. For nonfringe images, experimental results show that the correlation coefficient between registered images and the reference image increases from 0.5 to above 0.9. For fringe images , the calculation accuracy of wrapped phase improves significantly. Therefore, the algorithm in this paper effectively solves the registration problem of 2D-VISAR four-phase images, laying a foundation for further data processing in the future.
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Key words:
- images registration /
- SIFT algorithm /
- 2D-VISAR /
- four-phase images /
- interference fringes
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图 3 基于SIFT算法的二维VISAR四相位图像配准流程图
Figure 3. Flowchart of 2D-VISAR four-phase images registration based on SIFT
表 1 无条纹图像配准前后图像相关系数
Table 1. Correlation coefficient of non-fringe images before registration and after registration
I2 I3 I4 before registration 0.5752 0.5707 0.5383 after registration 0.9272 0.8721 0.9039 
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[1] Lindl J. Development of the indirect-drive approach to inertial confinement fusion and the target physics basis for ignition and gain[J]. Physics of Plasmas, 1995, 2(11): 3933-4024. doi: 10.1063/1.871025 [2] Sacks R A, Darling D H. Direct drive cryogenic ICF capsules employing D-T wetted foam[J]. Nuclear Fusion, 1987, 27(3): 447-452. doi: 10.1088/0029-5515/27/3/009 [3] Hurricane O A, Callahan D A, Casey D T, et al. Fuel gain exceeding unity in an inertially confined fusion implosion[J]. Nature, 2014, 506(7488): 343-348. doi: 10.1038/nature13008 [4] Celliers P M, Erskine D J, Sorce C M, et al. A high-resolution two-dimensional imaging velocimeter[J]. Review of Scientific Instruments, 2010, 81: 035101. doi: 10.1063/1.3310076 [5] Ali S J, Celliers P M, Haan S, et al. Probing the seeding of hydrodynamic instabilities from nonuniformities in ablator materials using 2D velocimetry[J]. Physics of Plasmas, 2018, 25: 092708. doi: 10.1063/1.5047943 [6] 黄海波, 李晓玲, 熊文怡, 等. 基于SIFT算法的图像配准综述[J]. 软件导刊, 2019, 18(1):1-4Huang Haibo, Li Xiaoling, Xiong Wenyi, et al. A survey of image registration based on SIFT[J]. Software Guide, 2019, 18(1): 1-4 [7] 李云红, 刘宇栋, 苏雪平, 等. 红外与可见光图像配准技术研究综述[J]. 红外技术, 2022, 44(7):641-651Li Yunhong, Liu Yudong, Su Xueping, et al. Review of infrared and visible image registration[J]. Infrared Technology, 2022, 44(7): 641-651 [8] Lowe D G. Object recognition from local scale-invariant features[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. 1999: 1150-1157. [9] 黄敏. 基于特征点的图像配准技术研究[D]. 南昌: 南昌大学, 2021Huang Min. Research on image registration technology based on feature points[D]. Nanchang: Nanchang University, 2021 [10] 夏晓华, 赵倩, 向华涛, 等. 多聚焦图像离焦模糊区域的SIFT特征提取[J]. 光学 精密工程, 2023, 31(24):3630-3639 doi: 10.37188/OPE.20233124.3630Xia Xiaohua, Zhao Qian, Xiang Huatao, et al. SIFT feature extraction method for the defocused blurred area of multi-focus images[J]. Optics and Precision Engineering, 2023, 31(24): 3630-3639 doi: 10.37188/OPE.20233124.3630 [11] 郝帅, 吴瑛琦, 马旭, 等. 基于CycleGAN-SIFT的可见光和红外图像匹配[J]. 光学 精密工程, 2022, 30(5):602-614 doi: 10.37188/OPE.20223005.0602Hao Shuai, Wu Yingqi, Ma Xu, et al. Visible and infrared image matching based on CycleGAN-SIFT[J]. Optics and Precision Engineering, 2022, 30(5): 602-614 doi: 10.37188/OPE.20223005.0602 [12] 丁国绅, 乔延利, 易维宁, 等. 基于光谱图像空间的F-SIFT特征提取与匹配[J]. 光学 精密工程, 2021, 29(5):1180-1189 doi: 10.37188/OPE.20212905.1180Ding Guoshen, Qiao Yanli, Yi Weining, et al. Feature extraction and matching of F-SIFT based on spectral image space[J]. Optics and Precision Engineering, 2021, 29(5): 1180-1189 doi: 10.37188/OPE.20212905.1180 [13] 谢永清. 基于尺度不变特征变换算法实现图像匹配与校正的研究[D]. 北京: 北京邮电大学, 2022Xie Yongqing. Research on image matching and correction based on scale invariant feature transformation[D]. Beijing: Beijing University of Posts and Telecommunications, 2022 [14] 曾峦, 王元钦, 谭久彬. 改进的SIFT特征提取和匹配算法[J]. 光学 精密工程, 2011, 19(6):1391-1397 doi: 10.3788/OPE.20111906.1391Zeng Luan, Wang Yuanqin, Tan Jiubin. Improved algorithm for SIFT feature extraction and matching[J]. Optics and Precision Engineering, 2011, 19(6): 1391-1397 doi: 10.3788/OPE.20111906.1391 [15] 曾文锋, 李树山, 王江安. 基于仿射变换模型的图像配准中的平移、旋转和缩放[J]. 红外与激光工程, 2001, 30(1):18-20,17Zeng Wenfeng, Li Shushan, Wang Jiangan. Translation, rotation and scaling changes in image registration based affine transformation model[J]. Infrared and Laser Engineering, 2001, 30(1): 18-20,17 [16] Yue Yongdou, Yu Jianqiao, Wei Yuyong, et al. A improved CoSaMP algorithm based on correlation coefficient for compressed sensing image reconstruction[J]. Journal of Computational Information Systems, 2013, 9(18): 7325-7331. -
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