Liang Cheng, Pu Fangyuan, Liang Lei, et al. Pressure sensitive paint image registration combined with gray level information[J]. Opto-Electronic Engineering, 2019, 46(2): 180301. doi: 10.12086/oee.2019.180301
Citation: Liang Cheng, Pu Fangyuan, Liang Lei, et al. Pressure sensitive paint image registration combined with gray level information[J]. Opto-Electronic Engineering, 2019, 46(2): 180301. doi: 10.12086/oee.2019.180301

Pressure sensitive paint image registration combined with gray level information

    Fund Project: Supported by the Ministry of Education Chunhui Project (z2016149) and Xihua University Key Laboratory Development Program (szjj2017-065)
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  • 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 measurement 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 relationship 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%.
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  • Overview: In recent years, with the rapid development of China′s aviation industry, higher requirements have been put forward for designing and manufacturing new types of aircraft. The wind tunnel pressure test is a key link in design and manufacturing. The current popular method is to use pressure-sensitive paint technology with the advantages of economy and speed for pressure measurement. The principle is that the pressure-sensitive paint will perform a dynamic oxidation quenching reaction according to the surface pressure of the test model, thus emitting light with different intensities. The two images of the test model under windy and windless conditions are taken and calculated using the Stern-Volmer formula. The corresponding pixel pressure data is obtained, and the two-dimensional pressure distribution of the entire model surface can be obtained. However, due to the existence of aerodynamic loads in the actual test, deformation and displacement of the test model will occur. In particular, under strong wind speed conditions, the test model will easily undergo elastic deformation and the resulting image will also be distorted. Therefore, registration of windy images is required. However, current algorithms for registering pressure-sensitive paint images have more or less partial disadvantages. For example, it is hard to achieve registration of image details for the registration of the model using only a rigid transformation, or simple non-rigid transformation registration of the model as a whole (affine transformation or polynomial transformation). This paper innovatively uses the two-dimensional non-rigid iterative closest point (ICP) algorithm to solve those problems. 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 traditional two-dimensional non-rigid ICP algorithm, only the two-dimensional coordinate position relationship is considered, and the correlation of pixel grayscales of the pressure-sensitive paint image is ignored. Thus, 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 accuracy of registration, this paper proposes an improved non-rigid ICP algorithm based on pixel association 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, RMSE increased by more than 15%, and NMI increased by about 5%.

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