  To address the problem of the low accuracy of transverse velocity field
measurements for small targets in high-resolution solar images, we proposed a
novel velocity field measurement method for high-resolution solar images based
on PWCNet. This method transforms the transverse velocity field measurements
into an optical flow field prediction problem. We evaluated the performance of
the proposed method using the Ha and TiO datasets obtained from New Vacuum
Solar Telescope (NVST) observations. The experimental results show that our
method effectively predicts the optical flow of small targets in images
compared with several typical machine- and deep-learning methods. On the Ha
dataset, the proposed method improves the image structure similarity from
0.9182 to 0.9587 and reduces the mean of residuals from 24.9931 to 15.2818; on
the TiO dataset, the proposed method improves the image structure similarity
from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to
17.0194. The optical flow predicted using the proposed method can provide
accurate data for the atmospheric motion information of solar images. The code
implementing the proposed method is available on
https://github.com/lygmsy123/transverse-velocity-field-measurement.
