When observing an object horizontally at a long distance, degradations due to atmospheric turbulence often
occur. In our previous work, we tried different methods to get rid of these degradations on infrared sequences.
We showed that the Wiener filter applied locally on each frame of a sequence allows to obtain good results in
terms of edges, while the regularization by the Laplacian operator applied in the same way provides good results
in terms of noise removal in uniform areas. In this article, we try to combine the results of these two methods
in order to obtain a better restoration image.
The main image degradation occuring in long distance ground-to-ground infrared video acquisition is due to atmospheric turbulence. The turbulence strength essentially depends on climatic conditions and on the distance between the scene and the camera. Atmospheric turbulence can show dramatically different effects, but in the case of horizontal observations in the troposphere, at a distance over a couple of kilometers, it can be efficiently simulated by local blurring and warping. Some additive noise may be detected depending on atmospheric conditions and on the acquisition system. In the acquisition conditions, the degraded images can be split into areas degraded by the same perturbation, which is called <i>local isoplanatism.</i>The goal of this paper is to test locally the most classical restoration methods on real images, in order to deduce some criterion allowing selection of the most suited method. The first part of the paper is devoted to the physical explanation of local isoplanatism (LI). In the second part, once the case study has been shown to fit with LI assumptions, we show that global restoration techniques do not work properly compared to local ones. Then local restoration results are analysed within uniform areas and transition areas so as to find the best restoration technique. Several examples are shown.