Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics. This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field, addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images. The proposed method converts the transverse velocity field computation problem into an optical flow computation problem, using two forward propagations of features to get rid of the reliance on optical flow labels. Additionally, it reduces the impact of the "Brightness Consistency" constraint on optical flow accuracy by identifying and handling optical flow outliers. We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hα and TiO bands, observed by the New Vacuum Solar Telescope. Comparative experiments with several well-established optical flow methods, including those based on supervised deep learning models, show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean, Residual Map Variance, Cross Correlation, and Structural Similarity Index Measure. Moreover, since optical flow captures the fundamental motion information in image sequences, the proposed method can be applied to a variety of research areas, including solar image registration, sequence alignment, image super-resolution, magnetic field calibration, and solar activity forecasting. The code is available at https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.