Acta Optica Sinica, Volume. 44, Issue 9, 0915002(2024)

Unsupervised Learning Based Image Registration of Wind Tunnel Pressure Sensitive Paint Image

Kang Liu1,2,3, Xiongwei Sun1,2,3、*, Hailiang Shi1,2,3、**, Xianhua Wang1,2,3, Hanhan Ye2,3, Chen Cheng1,2,3, Feng Zhu2,3, and Shichao Wu2,3
Author Affiliations
  • 1University of Science and Technology of China, Hefei 230026, Anhui, China
  • 2Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui, China
  • 3Key Laboratory of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences, Hefei 230031, Anhui, China
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    References(30)

    [1] Egami Y, Hasegawa Y, Matsuda Y et al. Ruthenium-based fast-responding pressure-sensitive paint for measuring small pressure fluctuation in low-speed flow field[J]. Measurement Science and Technology, 32, 024003(2021).

    [2] Running C L, Sakaue H, Juliano T J. Hypersonic boundary-layer separation detection with pressure-sensitive paint for a cone at high angle of attack[J]. Experiments in Fluids, 60, 23(2019).

    [3] Disotell K J, Nikoueeyan P, Naughton J W et al. Global surface pressure measurements of static and dynamic stall on a wind turbine airfoil at low Reynolds number[J]. Experiments in Fluids, 57, 82(2016).

    [4] Sugioka Y, Koike S, Nakakita K et al. Experimental analysis of transonic buffet on a 3D swept wing using fast-response pressure-sensitive paint[J]. Experiments in Fluids, 59, 108(2018).

    [5] Friedl F, Krah N, Jähne B. Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance[J]. Sensors and Actuators B, 206, 336-342(2015).

    [6] Xiang Y M, Wang F, You H J. OS-SIFT: a robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 3078-3090(2018).

    [7] Studholme C, Drapaca C, Iordanova B et al. Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change[J]. IEEE Transactions on Medical Imaging, 25, 626-639(2006).

    [8] Chu G H, Fan D Z, Dong Y et al. A cross-source image point cloud registration method combined with graph theory[J]. Acta Optica Sinica, 43, 1228006(2023).

    [9] Jia R Q, Yin G F, Zhao N J et al. Registration method of microscopic bright field and fluorescence synchronous measurement images of phytoplankton cells[J]. Chinese Journal of Lasers, 49, 2407202(2022).

    [10] Shen C M, Liu F, Zhu J L. Back point cloud registration algorithm based on structured light and CT[J]. Laser & Optoelectronics Progress, 60, 2210007(2023).

    [11] Dong Y Y, Jiao W L, Long T F et al. An extension of phase correlation-based image registration to estimate similarity transform using multiple polar Fourier transform[J]. Remote Sensing, 10, 1719(2018).

    [12] Liu T S, Sullivan J P, Asai K et al[M]. Pressure and temperature sensitive paints(2021).

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

    [14] Suzuki K, Inoue T, Nagata T et al. Markerless image alignment method for pressure-sensitive paint image[J]. Sensors, 22, 453(2022).

    [15] Cao C H, Cao L, Li G et al. BIRGU Net: deformable brain magnetic resonance image registration using gyral-net map and 3D Res-Unet[J]. Medical & Biological Engineering & Computing, 61, 579-592(2023).

    [16] Li W J, Kong D Q, Cao G G et al. 2D-3D medical image registration based on training-inference decoupling architecture[J]. Laser & Optoelectronics Progress, 59, 1610015(2022).

    [17] Lin L H, Yi J B, Cao F et al. Non-rigid registration algorithm of lung computed tomography image based on multi-scale parallel fully convolutional neural network[J]. Laser & Optoelectronics Progress, 59, 1617004(2022).

    [18] Liu L, Li Y X, Ni R S et al. Synthetic aperture radar and optical images registration based on convolutional and graph neural networks[J]. Acta Optica Sinica, 42, 2410002(2022).

    [19] Haskins G, Kruger U, Yan P K. Deep learning in medical image registration: a survey[J]. Machine Vision and Applications, 31, 8(2020).

    [20] Yang X, Kwitt R, Styner M et al. Quicksilver: fast predictive image registration: a deep learning approach[J]. NeuroImage, 158, 378-396(2017).

    [21] Rohé M M, Datar M, Heimann T, Descoteaux M, Maier-Hein L, Franz A et al. SVF-net: learning deformable image registration using shape matching[M]. Medical image computing and computer assisted intervention-MICCAI 2017. Lecture notes in computer science, 10433, 266-274(2017).

    [23] Kim B, Kim D H, Park S H et al. CycleMorph: cycle consistent unsupervised deformable image registration[J]. Medical Image Analysis, 71, 102036(2021).

    [24] de Vos B D, Berendsen F F, Viergever M A, Cardoso M J, Arbel T, Carneiro G et al. End-to-end unsupervised deformable image registration with a convolutional neural network[M]. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 10553, 204-212(2017).

    [25] Balakrishnan G, Zhao A, Sabuncu M R et al. An unsupervised learning model for deformable medical image registration[C], 9252-9260(2018).

    [26] Balakrishnan G, Zhao A, Sabuncu M R et al. VoxelMorph: a learning framework for deformable medical image registration[J]. IEEE Transactions on Medical Imaging, 38, 1788-1800(2019).

    [27] Han R, Jones C K, Lee J et al. Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance[J]. Medical Image Analysis, 75, 102292(2022).

    [29] Ronneberger O, Fischer P, Brox T, Navab N, Hornegger J, Wells W M et al. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

    [30] Ho T T, Kim W J, Lee C H et al. An unsupervised image registration method employing chest computed tomography images and deep neural networks[J]. Computers in Biology and Medicine, 154, 106612(2023).

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    Kang Liu, Xiongwei Sun, Hailiang Shi, Xianhua Wang, Hanhan Ye, Chen Cheng, Feng Zhu, Shichao Wu. Unsupervised Learning Based Image Registration of Wind Tunnel Pressure Sensitive Paint Image[J]. Acta Optica Sinica, 2024, 44(9): 0915002

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    Paper Information

    Category: Machine Vision

    Received: Dec. 4, 2023

    Accepted: Feb. 23, 2024

    Published Online: May. 15, 2024

    The Author Email: Xiongwei Sun (xiongweisun@163.com), Hailiang Shi (hlshi@aiofm.ac.cn)

    DOI:10.3788/AOS231885

    CSTR:32393.14.AOS231885

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