title: Atmospheric turbulence mitigation using complex wavelet-based fusion.
abstract: 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 good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement 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 significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios.
authors.affiliation: Visual Information Laboratory, University of Bristol, Bristol BS8 1UB, UK. n.anantrasirichai@bris.ac.uk
authors.forename: Nantheera
authors.initials: N
authors.lastname: Anantrasirichai
authors.forename: Alin
authors.initials: A
authors.lastname: Achim
authors.forename: Nick G
authors.initials: NG
authors.lastname: Kingsbury
authors.forename: David R
authors.initials: DR
authors.lastname: Bull