A comparison of different atmospheric turbulence simulation methods for image restoration

TL;DR

This paper systematically evaluates how different atmospheric turbulence simulation methods impact the effectiveness of deep learning-based image restoration, guiding future research in selecting appropriate data generation models.

Contribution

It provides a comprehensive comparison of six turbulence simulation methods on real-world face images, assessing their influence on restoration network performance.

Findings

Simulation method choice significantly affects restoration quality.

State-of-the-art networks perform variably across different simulations.

Guidance provided for selecting suitable data generation models.

Abstract

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.