Atmospheric turbulence adversely affects imaging systems by causing a random distribution of the index of refraction of the air through which the light must propagate. The resulting image degradation can seriously undermine the effectiveness of the sensor. In many astronomical systems, which typically have a very narrow field of view, the entire image can be modeled by the convolution of the object with a single point spread function (PSF), and as a result of the narrow field of view, adaptive optical systems can be highly effective in correcting astronomical images. In the case of tactical infrared sensors the field of view is generally much larger than the isoplanatic angle, and the image cannot be modeled by a single point spread function convolved with the scene. Hence, adaptive optical solutions to wide angle infrared imaging over horizontal paths would be difficult, if not impossible, and post-detection processing of the images is required to mitigate turbulence effects. The overall effect of turbulence within a given isoplanatic path is not as strong as in the astronomical imaging case due to shorter paths and longer wavelengths. Tilt and low order turbulence modes dominate the aberration experienced within individual isoplanatic patches, greatly simplifying image reconstruction problems. In this paper we describe an algorithm for processing video sequences capable of partially correcting these turbulence effects. The algorithm is based on block matching algorithms used in video compression. Simulation results show that this algorithm reduces the squared error of the imagery, and subjectively better images are obtained.