estimated movements of individual pixels can sometimes be much larger, and the mean displacement of each pixel may deviate more significantly from zero in a real turbulence-distorted video. It may pose a challenge for the centroid method to remove all the geometric distortions. Another approach to obtain a clear reference image is done by selecting a “lucky frame”, which is the sharpest frame from a distorted video [@vorontsov2001anisoplanatic]. This method is motivated and supported by the statistical proofs [@fried1978probability] that show a high probability of extracting video frames with sharp texture details given a sufficient amount of frames. Nevertheless, in many situations, getting a frame that is entirely sharp everywhere is difficult. To alleviate this issue, the Lucky-Region method has been proposed by Aubailly [*et al.*]{} [@aubailly2009automated], which chooses the sharpest patch from each frame and combine them afterward. Motivated by this patch-wise sharpness selection method, another approach introduced by Anantrasirichai [*et al.*]{} [@anantrasirichai2013atmospheric] suggests to having a frame selection prior to registration. A composite cost function was introduced, and the selection was done in one step by sorting. However, some of the selected frame may geometrically differ significantly from the reference image. On the other hand, the cost function 