  Atmospheric turbulence deteriorates the quality of images captured by
long-range imaging systems by introducing blur and geometric distortions to the
captured scene. This leads to a drastic drop in performance when computer
vision algorithms like object/face recognition and detection are performed on
these images. In recent years, various deep learning-based atmospheric
turbulence mitigation methods have been proposed in the literature. These
methods are often trained using synthetically generated images and tested on
real-world images. Hence, the performance of these restoration methods depends
on the type of simulation used for training the network. In this paper, we
systematically evaluate the effectiveness of various turbulence simulation
methods on image restoration. In particular, we evaluate the performance of two
state-or-the-art restoration networks using six simulations method on a
real-world LRFID dataset consisting of face images degraded by turbulence. This
paper will provide guidance to the researchers and practitioners working in
this field to choose the suitable data generation models for training deep
models for turbulence mitigation. The implementation codes for the simulation
methods, source codes for the networks, and the pre-trained models will be
publicly made available.
