[1] WILLERT C E, GHARIB M. Digital particle image velocimetry[J]. Experiments in Fluids, 1991, 10: 181-193.
[2] YU C D, CHANG Y P, LIANG X, et al. Deep learning for particle image velocimetry with attentional transformer and cross-correlation embedded[J]. Ocean Engineering, 2024, 292: 116522.
[3] OZAWA Y, HONDA H, NONOMURA T. Spatial super resolution based on simultaneous dual PIV measurement with different magnification[J]. Experiments in Fluids, 2024, 65: 42.
[4] 毕晓君,何明洁,于长东, 等.基于深度学习的液相流粒子图像测速估计[J].哈尔滨工程大学学报, 2023, 44(4):622-630.
BI X J, HE M J, YU C D, et al. Particle image velocimetry for liquid phase flow based on deep learning [J]. Journal of Harbin Engineering University, 2023, 44(4):622-630. (in Chinese)
[5] WENG W G, FAN W C, LIAO G X, et al. Wavelet-based image denoising in (digital) particle image velocimetry[J]. Signal Processing, 2001, 81(7): 1503-1512.
[6]DE LIMA AMARAL R, BORTOLIN V A A, LEMOS B L H D, et al. A novel method based on the Otsu threshold for instantaneous elimination of light reflection in PIV images[J]. Measurement Science and Technology, 2021, 33(2): 025401.
[7] STANISLAS M, OKAMOTO K, KÄHLER C. Main results of the first international PIV challenge[J]. Measurement Science and Technology, 2003, 14(10): R63.
[8] SCIACCHITANO A, SCARANO F. Elimination of PIV light reflections via a temporal high pass filter[J]. Measurement Science and Technology, 2014, 25(8): 084009.
[9] HONKANEN M, NOBACH H. Background extraction from double-frame PIV images[J]. Experiments in Fluids, 2005, 38: 348-362.
[10] DEEN N G, WILLEMS P, VAN SINT ANNALAND M, et al. On image pre-processing for PIV of single-and two-phase flows over reflecting objects[J]. Experiments in Fluids, 2010, 49: 525-530.
[11] MENDEZ M A, RAIOLA M, MASULLO A, et al. POD-based background removal for particle image velocimetry[J]. Experimental Thermal and Fluid Science, 2017, 80: 181-192.
[12] ADATRAO S, SCIACCHITANO A. Elimination of unsteady background reflections in PIV images by anisotropic diffusion[J]. Measurement Science and Technology, 2019, 30(3): 035204.
[13] CAI S Z, ZHOU S C, XU C, et al. Dense motion estimation of particle images via a convolutional neural network[J]. Experiments in Fluids, 2019, 60: 1-16.
[14] 于长东, 毕晓君, 韩阳, 等.基于轻量化深度学习模型的粒子图像测速研究[J]. 光学学报, 2020, 40(7):142-149.
YU C D, BI X J, HAN Y, et al. Particle image velocimetry based on a lightweight deep learning model [J]. Acta Optica Sinica, 2020, 40(7):142-149. (in Chinese)
[15] YU C D, BI X J, FAN Y W, et al. LightPIVNet: An effective convolutional neural network for particle image velocimetry[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-15.
[16] FAN Y W, GUO C Y, HAN Y, et al. Deep-learning-based image preprocessing for particle image velocimetry[J]. Applied Ocean Research, 2023, 130: 103406.
[17] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE,2016: 770-778.
[18] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning. PMLR, 2015: 448-456.
[19] ZHANG K, ZUO W M, CHEN Y J, et al. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155.
[20] ADRIAN R J. Particle-imaging techniques for experimental fluid mechanics[J]. Annual Review of fluid Mechanics, 1991, 23(1): 261-304.
[21] LEE Y, YANG H, YIN Z P. PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry[J]. Experiments in Fluids, 2017, 58: 1-10.
[22] THIELICKE W, STAMHUIS E J. PIVlab–towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB[J]. Journal of Open Research Software, 2014, 2: 30.
[23]WANG C X, JIN Z. Brighten-and-colorize: A decoupled network for customized low-light image enhancement[C]//Proceedings of the 31st ACM International Conference on Multimedia. 2023: 8356-8366.
[24] SCARANO F. Iterative image deformation methods in PIV[J]. Measurement Science and Technology, 2001, 13(1): R1.
[25] WESTERWEEL J, SCARANO F. Universal outlier detection for PIV data[J]. Experiments in Fluids, 2005, 39: 1096-1100.