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。

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%.