Development of Mathematical Methods and Algorithms for Filtering Images Obtained from Unmanned Aerial Vehicle Camera

Recently, the use of unmanned aerial vehicles (UAVs) has become an interesting and active research topic in the field of aerial photography. Such an increase in popularity makes the task of finding optimal hardware and software configurations for UAVs, developing systems for compensating adverse environmental effects, and navigation systems in space, etc. The key factors behind this trend are the relatively low cost of implementing such projects and the speed of obtaining data. The main problems preventing full automation of data processing of UAV imagery are motion blur with a still camera, full image blur with camera movement, and camera focus blur. When shooting from a UAV in bad weather conditions, blurry video frames often occur that require image filter. This blurring can interfere with visual analysis and interpretation of data, cause errors, and reduce the accuracy of automatic photogrammetric processing algorithms. This article describes the proposed algorithm that performs element-by-element image filtering based on the use of neural-like structures.