Wiener filter and linear-MVUE for feature point extraction in atmospheric turbulence image

In the process of imaging, atmospheric turbulence will lead to image degradation, such as noise, blur, geometric distortion, thus reducing the quality of feature point extraction. In order to solve this problem, we analyze images with atmospheric turbulence degradation and find that image blur and geometric distortion have great influence on feature extraction. Image blur is a representation of high-frequency information loss, so detectors based on gray gradient will extract fewer points. On the other hand, geometric distortion is reflected by the movement of pixels in the image patch, which will also cause the movement of feature points, especially when they are extracted according to their neighborhoods. In this paper, we propose Wiener Filter and Linear Minimum Variance Unbiased Estimation (WFLMVUE) strategy to deal with image blur and geometric distortion respectively. A simplified filter based on Wiener's method is used to remove noise and ambiguity. Then the base frame and auxiliary frames are used to estimate the position of feature points by linear minimum variance unbiased estimation. Experimental results show that WF-LMUVE has great advantages in increasing the number of feature points and improving their location accuracy.