  The analysis of the photospheric velocity field is essential for
understanding plasma turbulence in the solar surface, which may be responsible
for driving processes such as magnetic reconnection, flares, wave propagation,
particle acceleration, and coronal heating. Currently, the only available
methods to estimate velocities at the solar photosphere transverse to an
observer's line of sight infer flows from differences in image structure in
successive observations. Due to data noise, algorithms such as local
correlation tracking (LCT) may lead to a vector field with wide gaps where no
velocity vectors are provided. In this letter, a novel method for image
inpainting of highly corrupted data is proposed and applied to the restoration
of horizontal velocity fields in the solar photosphere. The restored velocity
field preserves all the vector field components present in the original field.
The method shows robustness when applied to both simulated and observational
data.
