Quantitative evaluation of turbulence compensation

Abstract

A well-known phenomena that diminishes the recognition range in infrared imagery is atmospheric turbulence. In literature many methods are described that try to compensate for the distortions caused by atmospheric turbulence. Most of these methods use a global processing approach in which they assume a global shift and a uniform blurring in all frames. Because the effects of atmospheric turbulence are often spatial and temporal varying, we presented previous year a turbulence compensation method that performs local processing leading to excellent results. In this paper an improvement of this method is presented which uses a temporal moving reference frame in order to be capable of processing imagery containing moving objects as well as blur estimation to obtain adaptive deconvolution. Furthermore our method is evaluated in a quantitative way, which will give a good insight in which components of our method contribute to the obtained visual improvements.

Evaluation of turbulence mitigation methods

“…An overview of this method is depicted in Figure 2 and previously described in 5 . The different steps are explained in more detail in the subsections below.…”

“…In this paper we will focus on two software approaches that are capable of processing static and dynamic scenes. The first method 5 consists of local registration, frame selection, blur estimation and deconvolution. The second method 6,7 consists of local motion compensation, fore-/background segmentation and weighted iterative blind deconvolution.…”