  In many practical applications of long-range imaging such as biometrics and
surveillance, thermal imagining modalities are often used to capture images in
low-light and nighttime conditions. However, such imaging systems often suffer
from atmospheric turbulence, which introduces severe blur and deformation
artifacts to the captured images. Such an issue is unavoidable in long-range
imaging and significantly decreases the face verification accuracy. In this
paper, we first investigate the problem with a turbulence simulation method on
real-world thermal images. An end-to-end reconstruction method is then proposed
which can directly transform thermal images into visible-spectrum images by
utilizing natural image priors based on a pre-trained StyleGAN2 network.
Compared with the existing two-steps methods of consecutive turbulence
mitigation and thermal to visible image translation, our method is demonstrated
to be effective in terms of both the visual quality of the reconstructed
results and face verification accuracy. Moreover, to the best of our knowledge,
this is the first work that studies the problem of thermal to visible image
translation under atmospheric turbulence.
