Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical.
In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from goodquality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the spacevarying distortion problem using region-level fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). Finally, haze removal is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full- and no-reference scenarios. The proposed method is shown to clearly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios.