结合灰度信息的压敏漆图像 配准

压敏漆技术是一种经济性高、速度快的风洞测压前沿技术。在风洞试验中，由于强风影响，模型会发生畸变，造成有风图像和无风图像难以配准，从而严重影响测压精度。针对这一问题，本文创新性的将二维非刚性 ICP算法用于此问题，采用点云方式使得图像细节区域有效配准，同时也有利于后续三维重建工作。然而由于二维非刚性 ICP算法仅考虑二维坐标位置关系，忽略压敏漆图像像素灰度具有的相关性，使得配准精度不高。直接利用三维非刚性 ICP算法又会发生误配准，所以为了进一步提高配准精度，本文提出了一种基于像素关联搜索策略的非刚性 ICP算法，算法设计了综合考虑 2D坐标与像素灰度值的双目标搜索策略，实现了精确的局部匹配点搜索与双目标优化。在多组压敏漆图像上将本文算法与五种配准算法进行了对比实验分析。实验结果表明，本文所提出的算法具有最好的配准精度。相比次优算法，RMSE提升超过 15%，NMI提升在 5%左右。

Abstract

Pressure-sensitive paint technology is a wind tunnel pressure measurement frontier technology with high economical efficiency and high speed. In the wind tunnel test, due to the strong wind, the model will be distorted, making the wind image and the windless image difficult to register, which will seriously affect the pressure mea-surement accuracy. In response to this problem, this paper innovatively applies the two-dimensional non-rigid iterative closest point (ICP) algorithm to solve this problem. The point cloud method is used to make the image detail area to be effectively registered, and it is also conducive to the subsequent three-dimensional reconstruction work. However, due to the two-dimensional non-rigid ICP algorithm, only the two-dimensional coordinate positional rela-tionship is considered. The correlation of the pixel grayscales of the pressure-sensitive paint image is neglected, so that the registration accuracy is not too high. However, if the three-dimensional non-rigid ICP algorithm is directly used, misregistration will occur. Therefore, in order to further improve the registration accuracy, this paper proposes a non-rigid ICP algorithm based on pixel-based search strategy. The algorithm designs a dual-target search strategy that takes 2D coordinates and pixel gray values into consideration and achieves accurate local matching, realizing point search and double goal optimization. The algorithm of this paper is compared with five registration algorithms on multiple sets of pressure sensitive paint images. The experimental results show that the proposed algorithm has the best registration accuracy. Compared to the suboptimal algorithm, the RMSE is improved by more than 15% and the NMI is increased by about 5%.

参考文献

[1] Friedl F, Krah N, J.hne B. Optical sensing of oxygen using a modified stern–volmer equation for high laser irradiance[J]. Sensors and Actuators B: Chemical, 2015, 206: 336–342.

[2] Peng D, Chen J W, Jiao L R, et al. A fast-responding semi-transparent pressure-sensitive paint based on through-hole anodized Aluminum oxide membrane[J]. Sensors and Actuators A: Physical, 2018, 274: 10–18.

[3] Kontis K. A review of some current research on pressure sen-sitive paint and thermographic phosphor techniques[J]. The Aeronautical Journal, 2007, 111(1122): 495–508.

[4] Park S H, Sung H J. Correlation-based image registration for applications using pressure-sensitive paint[J]. AIAA Journal, 2005, 43(3): 472–478.

[5] Fujimatsu N, Tamura Y, Fujii K. Improvement of noise filtering and image registration methods for the pressure sensitive paint experiments[J]. Journal of Visualization, 2005, 8(3): 225–233.

[6] Chen C, Chen Y, Huang Q. Study of image geometric distortion correction for PSP measurements[J]. Computer Engineering and Design, 2010, 31(24): 5298–5301. 陈畅, 陈勇, 黄琦. 压敏漆技术中的图像几何畸变校正研究 [J].计算机工程与设计, 2010, 31(24): 5298–5301.

[7] Gao L M, Wei N,Gao J, et al. Image processing of PSP tech-nique in the internal flow[J]. Journal of Experiments in Fluid Mechanics, 2013, 27(1): 93–97.高丽敏, 韦楠, 高杰, 等. 基于内流场 PSP测量技术的图像后处理[J].实验流体力学, 2013, 27(1): 93–97.

[8] Xiong H L, Xiang X J, Lang W D. Image data processing tech-niques for pressure sensitive paint[J]. Journal of Experiments in Fluid Mechanics, 2011, 25(2): 77–82. 熊红亮 , 向星居 , 郎卫东 . 压敏漆图像数据处理技术 [J]. 实验流体力学, 2011, 25(2): 77–82.

[9] Lu X S, Zhang S, Yang W, et al. SIFT and shape information incorporated into fluid model for non-rigid registration of ultra-sound images[J]. Computer Methods and Programs in Biome-dicine, 2010, 100(2): 123–131.

[10] Aganj I, Iglesias E I, Reuter M, et al. Mid-space-independent deformable image registration[J]. Neuroimage, 2017, 152: 158–170.

[11] Besl P J, McKay N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 1992, 14(2): 239–256.

[12] Chi Y, Yu X Q, Luo Z Y. 3D point cloud matching based on principal component analysis and iterative closest point algo-rithm[C]//Proceedings of 2016 International Conference on Au-dio, Language and Image Processing, Shanghai, China, 2016: 404–408.

[13] Amberg B, Romdhani S, Vetter T. Optimal step nonrigid ICP algorithms for surface registration[C]//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007: 1–8.

[14] Hontani H, Matsuno T, Sawada Y. Robust nonrigid ICP using Outlier-Sparsity regularization[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 174–181.

[15] Cheng S Y, Marras I, Zafeiriou S, et al. Statistical non-rigid ICP algorithm and its application to 3D face alignment[J]. Image and Vision Computing, 2017, 58: 3–12.

[16] Chen J, Ma J Y, Yang C C, et al. Non-rigid point set registration via coherent spatial mapping[J]. Signal Processing, 2015, 106: 62–72.

[17] Lu M, Zhao J, Guo Y L, et al. Accelerated coherent point drift for automatic Three-Dimensional point cloud registration[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 162–166.

[18] Rueckert D, Sonoda L I, Hayes C, et al. Nonrigid registration using free-form deformations: application to breast MR im-ages[J]. IEEE Transactions on Medical Imaging, 1999, 18(8): 712–721.

[19] Woo J, Stone M, Prince J L. Multimodal registration via mutual information incorporating geometric and spatial context[J]. IEEE Transactions on Image Processing, 2015, 24(2): 757–769.

[20] Qiu Z Y, Tang H H, Tian D S. Non-rigid medical image registra-tion based on the thin-plate spline algorithm[C]//Proceedings of 2009 WRI World Congress on Computer Science and Informa-tion Engineering, Los Angeles, CA, USA, 2009: 522–527.

[21] Chan T F, Sandberg B Y, Vese L A. Active contours without edges for vector-valued images[J]. Journal of Visual Commu-nication and Image Representation, 2000, 11(2): 130–141.

[22] Xu L, Wan J W L, Bian T T. A continuous method for reducing interpolation artifacts in mutual Information-Based rigid image registration[J]. IEEE Transactions on Image Processing, 2013, 22(8): 2995–3007.

[23] Collignon A. Automated multimodality image registration using information theory[C]//Proceedings of International Conference Information Processing in Medical Imaging, Ile De Berder, France, 1995: 263–274.

[24] Viola P, Wells III W M. Alignment by maximization of mutual information[J]. International Journal of Computer Vision, 1997, 24(2): 137–154.

[25] Studholme C, Hill D L G, Hawkes D J. An overlap invariant entropy measure of 3D medical image alignment[J]. Pattern Recognition, 1999, 32(1): 71–86.

[26] Friedman J H, Bentley J L, Finkel R A. An algorithm for finding best matches in logarithmic expected time[J]. ACM Transac-tions on Mathematical Software, 1977, 3(3): 209–226.

梁诚, 蒲方圆, 梁磊, 高志升. 结合灰度信息的压敏漆图像配准[J]. 光电工程, 2019, 46(2): 180301. Liang Cheng, Pu Fangyuan, Liang Lei, Gao Zhisheng. Pressure sensitive paint image registration combined with gray level information[J]. Opto-Electronic Engineering, 2019, 46(2): 180301.

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