Evaluation of turbulence mitigation methods

Abstract

Atmospheric turbulence is a well-known phenomenon that diminishes the recognition range in visual and infrared image sequences. There exist many different methods to compensate for the effects of turbulence. This paper focuses on the performance of two software-based methods to mitigate the effects of low-and medium turbulence conditions. Both methods are capable of processing static and dynamic scenes. The first method consists of local registration, frame selection, blur estimation and deconvolution. The second method consists of local motion compensation, fore-/background segmentation and weighted iterative blind deconvolution. A comparative evaluation using quantitative measures is done on some representative sequences captured during a NATO SET 165 trial in Dayton. The amount of blurring and tilt in the imagery seem to be relevant measures for such an evaluation. It is shown that both methods improve the imagery by reducing the blurring and tilt and therefore enlarge the recognition range. Furthermore, results of a recognition experiment using simulated data are presented that show that turbulence mitigation using the first method improves the recognition range up to 25% for an operational optical system.

Turbulence mitigation methods and their evaluation

“…In an earlier paper 5 we compared two different turbulence mitigation methods (TNO and Fraunhofer IOSB) on a few image sequences containing the same amount of turbulence. In this paper we will describe two TNO turbulence mitigation methods and we will qualitatively evaluate their performance on three different image sequences containing varying amounts of turbulence.…”