  Atmospheric turbulence significantly affects imaging systems which use light
that has propagated through long atmospheric paths. Images captured under such
condition suffer from a combination of geometric deformation and space varying
blur. We present a deep learning-based solution to the problem of restoring a
turbulence-degraded face image where prior information regarding the amount of
geometric distortion and blur at each location of the face image is first
estimated using two separate networks. The estimated prior information is then
used by a network called, Turbulence Distortion Removal Network (TDRN), to
correct geometric distortion and reduce blur in the face image. Furthermore, a
novel loss is proposed to train TDRN where first and second order image
gradients are computed along with their confidence maps to mitigate the effect
of turbulence degradation. Comprehensive experiments on synthetic and real face
images show that this framework is capable of alleviating blur and geometric
distortion caused by atmospheric turbulence, and significantly improves the
visual quality. In addition, an ablation study is performed to demonstrate the
improvements obtained by different modules in the proposed method.
